# The sPHENIX Micromegas Outer Tracker

S. Aune  B. Azmoun  A. Bonenfant  S. Boose  M. Bregant  D. Cacace  R. W. da
Silva  R. Feder  A. Francisco  C. Goblin  A. Grabas  J. S. Haggerty  R. A.
Hernandez  H. D. H. Herrera  J. Huang  J. Kelsey  I. Kotov  J. Kuczewski  I.
Mandjavidze  T. A. Martins  J. Mead  J. Mills  A. Oskarsson  H. Pereira Da
Costa<EMAIL_ADDRESS>C. Pinkenburg  R. Pisani  T. Protzman
M. L. Purschke  E. Renner  R. Ruggiero  T. Sakaguchi  B. C. S. Sanches  B.
Sayki  D. Silvermyr  W. Sondheim  M. Vandenbroucke  W. A. M. Van Noije  J.
Vasquez  C. Vidal  A. Wils

###### Abstract

The sPHENIX Time Projection Chamber Outer Tracker (TPOT) is a Micromegas based
detector. It is a part of the sPHENIX experiment that aims to facilitate the
calibration of the Time Projection Chamber, in particular the correction of
the time-averaged and beam-induced distortions of the electron drift. This
paper describes the detector mission, setup, construction, installation,
commissioning and performance during the first year of sPHENIX data taking.

###### keywords:

RHIC , sPHENIX , Micro-pattern gaseous detectors , Micromegas , Resistive
anode , Zigzag pattern , Detector commissioning

[CEA] organization=CEA, Université Paris-Saclay, city=Gif-sur-Yvette,
country=France

[BNL] organization=Brookhaven National Laboratory, city=Upton, state=New York

[LANL] organization=Los Alamos National Laboratory, city=Los Alamos, state=New
Mexico

[LEHIGH] organization=Lehigh University, city=Bethlehem, state=Pennsylvania

[MIT] organization=Massachusetts Institute of Technology, city=Cambridge,
state=Massachusetts

[USP] organization=Universidade de São Paulo, city=São Paulo, country=Brazil

[LUND] organization=Lund University, city=Lund, country=Sweden

##  1 Introduction

The sPHENIX detector [1, 2] is an experiment located at the Relativistic Heavy
Ion Collider (RHIC) [3] in Brookhaven National Laboratory (BNL). It focuses on
measuring jets as well as open and hidden heavy flavor production in heavy ion
collisions and to study the properties of the Quark Gluon Plasma created in
gold on gold (${\rm Au+Au}$) collisions at a center of mass energy per
nucleon-nucleon collision ${\sqrt{s_{\rm NN}}}=200$ GeV. It will also collect
data using proton-proton (${p+p}$) and possibly proton-gold (${p+{\rm Au}}$)
collisions to serve as a reference to the ${\rm Au+Au}$ data and to study the
cold nuclear matter that constitutes the initial state of such collisions.

Figure 1: Schematic view of the sPHENIX detector, including all subsystems.
The TPOT detector appears in red. It is located between the EMCAL and the TPC,
at the bottom of the TPC.

Figure 1 presents a schematic view of the sPHENIX detector. The two beams
provided by RHIC collide at the center of the experiment. The products of the
collisions are measured in the various subsystems that constitute the
detector. Going outwards starting from the beam line, sPHENIX is comprised of
the following subsystems: the MAPS-Based Vertex Detector (MVTX); the
INTermediate Tracker (INTT); the Time Projection Chamber (TPC); the Time
Projection Chamber Outer Tracker (TPOT), which is the topic of this paper; the
Electromagnetic Calorimeter (EMCAL); the Inner Hadronic Calorimeter (IHCAL);
the solenoid superconducting magnet that delivers a longitudinal magnetic
field of intensity 1.4 T and the Outer Hadronic Calorimeter (OHCAL). The
tracking subsystems are the MVTX, INTT, TPC and TPOT. The calorimetry
subsystems are the EMCAL, IHCAL and OHCAL. sPHENIX also contains a number of
forward detectors, namely the Minimum Bias Detectors (MBD), the sPHENIX Event
Plane detectors (sEPD), the Zero Degree Calorimeters (ZDC) and the Shower
Maximum Detector (SMD), which is part of the ZDC.

Figure 2: Schematic view of the TPC calibration procedure using all available
outside detectors (INTT, MVTX and TPOT).

TPOT is a late addition to the sPHENIX apparatus. It is installed on the
outside of the TPC and greatly facilitates the calibration of the TPC. In
particular, it allows to make the maximum use of tracks for measuring in near
real time the distortions of the electron drift in the TPC during data taking.
It consists of 16 Micromesh Gaseous detectors (Micromegas) [4] grouped two-by-
two to provide an additional space point on the outside of the TPC in a
fraction of the TPC acceptance. Figure 2 presents a schematic view of how the
TPC can be calibrated using all of the other tracking detectors. A reference
track (in red) is built using the clusters measured in the MVTX, INTT and TPOT
(filled red point). It is interpolated inside the TPC volume and the expected
cluster positions are inferred (open red circles). For each TPC layer, the
expected position is compared to the measured clusters in the TPC and the
difference between the two is used to calibrate the TPC. This calibration is
extrapolated to the full acceptance of the TPC.

This paper describes the design, construction, testing, installation, and
commissioning of the TPOT detector. Section 2 is dedicated to the description
of the TPOT system (detector, electronics, and services); section 3 to the
detector installation inside sPHENIX and survey; and section 4 to the detector
characterization and performance.

##  2 System description

###  2.1 Requirements

The requirements on TPOT for it to be installed and operated as a part of
sPHENIX are listed below.

Available space: the purpose of TPOT is to provide an additional position
measurement to the reconstructed particles’ trajectories on the outside of the
TPC. It must therefore be located as close as possible to the outer radius of
the TPC. In the initial sPHENIX layout, there is a radial gap of 11 cm on
average between the TPC and the covers of the EMCAL sectors. This is the
maximum space available for TPOT. It puts strong constrains on the maximum
thickness of each detector layer, the number of layers that can be installed
and on the mechanical support structure for the detector.

Performance: because TPOT is not used as a measurement device critical for the
sPHENIX physics program but rather as a calibration device for the TPC, the
requirements on the detector in terms of detection efficiency and spatial
resolution are somewhat loose. A minimum of 90% detection efficiency is
required, together with a spatial resolution of a few 100 $\mu$m in both
azimuthal and longitudinal directions. The positioning of the detector itself
is not critical, and an accuracy of 1 mm or less is required. This is provided
that the real position (alignment) of the detector can be measured a
posteriori with a precision of 100 $\mu$m or less, significantly smaller than
the expected spatial resolution of the detector.

Cost and schedule: the decision to construct and install TPOT as a part of the
sPHENIX apparatus was taken late in the sPHENIX design process, namely about
eighteen months before the beginning of sPHENIX operations, and based on
design studies started approximately six months before that. This compressed
schedule, together with the total budget allocated to the project put strong
constraints on the amount of prototyping that could be performed before
starting the construction of the detector, as well as the number of modules
that could be built. In particular, it was decided early on that TPOT would
only cover a fraction of the TPC acceptance (Section 2.2). Extrapolation
methods would then be developed offline to extrapolate the calibrations
provided by TPOT to the rest of the TPC acceptance.

Magnetic field: TPOT is installed inside sPHENIX superconducting magnet, which
delivers an approximately uniform longitudinal magnetic field of intensity 1.4
T. The magnetic field directly impacts TPOT operation and in particular the
choice of gas that is used to operate the Micromegas chambers. In addition,
its presence prevents the use of any magnetic component for both the detector
and the mechanical support structure.

Particle rates and discharges: because of the requirements above in terms of
performances, cost, schedule and available space, it was realized early on
that the Micromegas technology [4] is a good candidate for building TPOT
(section 2.3). However such detectors are known to be susceptible to
electrostatic discharges when the number of electrons created in the avalanche
process responsible for the signal amplification exceeds the Raether limit [5]
of $2\times 10^{7}$ electrons. Based on simulations of the expected particle
rates in TPOT, the mean deposited energy of these particles, and for a typical
amplification gain of $6\times 10^{3}$, the rate of such electrostatic
discharge was estimated to be about 100 Hz. To prevent these discharges from
generating a prohibitively high dead-time in the detector or even damaging the
readout electronics, they must be quenched, in the TPOT case by adding a
resistive layer on top of the readout plane.

###  2.2 Detector segmentation

TPOT is made up of eight modules each consisting of two Micromegas chambers
stacked radially, one to measure the coordinates along the longitudinal
direction ($z$) and the second along the azimuthal direction ($\phi$). They
are referred to as the $z$ and $\phi$ views, respectively. The dimension of
each chamber’s active area is 256 mm along $\phi$ and 512 mm along $z$. The
signal on the $\phi$ views is collected on 256 strips oriented along $z$ and
with a pitch of 1 mm. The signal on the $z$ views is collected on 256 strips
oriented along $\phi$ and with a pitch of 2 mm. Four of the eight TPOT modules
cover the full $z$ extent of the bottom-most sector of the TPC (out of twelve
sectors total). The other four modules are located along the neighbor two
sectors (two modules each). With this configuration, the eight TPOT modules
cover approximately 8% of the TPC acceptance.

###  2.3 The Micromegas chambers

####  2.3.1 Principle

Figure 3 shows a schematic view of a bulk resistive Micromegas chamber. Each
chamber consists of a drift electrode, a micromesh, a resistive layer and a
readout layer arranged in parallel top to bottom. In order to guide electrons
through the drift (or conversion) gap, a negative electric field is applied
through the layers. The drift electrode is operated at negative voltage. The
micromesh is located 3 mm away from the drift electrode and connected to the
electrical ground. The resistive layer is located 128 $\mu$m away from the
micromesh and is operated at a positive voltage. The resistive layer is
segmented into four groups to be connected to the High Voltage (HV)
independently. This segmentation allows for mitigation of a HV short in one
part of the chamber while allowing the other 3/4th of the chamber to remain
active. The readout layer is located immediately below the resistive layer and
consists of 256 parallel strips per chamber. The strips read the signal
collected on the resistive layer through capacitive coupling and are connected
to the Front-End Electronics (FEE) boards.

Figure 3: Schematic view of one bulk, resistive Micromegas chamber.

To optimize the fabrication process, the chambers have no components soldered
on the readout board. The thickness of the readout Printed Circuit Board (PCB)
is 1.6 mm to match the SAMTEC MEC8 edge connector standard. One side of the
readout PCB is designed to form 4 MEC8 edge connectors to connect the 256
strips to the front-end electronics.

Connection to HV is established using a dedicated board, a FSI one-piece
compression connector from SAMTEC and its corresponding footprint on the
readout PCB. Such a system can stably support up to 2 kV voltage. The HV board
is shown in Figure 4. It houses the low-pass filter for five HV channels, four
of which are connected to the resistive layer and one to the drift electrode,
and the corresponding cables connected on the other end to the TPOT patch
panel.

Figure 4: Pictures of the High Voltage boards. Top: Top view; Center: bottom
view; bottom: HV board mounted on a Micromegas chamber.

The chambers are circulated with a 95/5% mixture of Ar/iC4H10 which provides
good gain and stability for TPOT operation. The distribution of the gas to the
inside of the chamber is carried out by the 3D-printed frame that also holds
the drift electrode and an O-ring seal for gas tightness. This 5 mm thick
frame cannot accommodate standard L-shape gas connectors. 3 mm diameter
polyurethane tubes glued directly inside the frame are used instead. The tubes
are capped with filters to ensure cleanliness inside the chambers at all times
(Figure 5). Each chamber is operated at a gas flow rate ranging from $50$ to
100 ccm and slightly above atmospheric pressure.

Figure 5: Left: picture of the gas inlet (yellow) and outlet (blue) on the two
chambers of a TPOT module. The chambers are connected in series, so that the
outlet of the first chamber is connected to the inlet of the second. Right:
gas inlet tube with its filter.

####  2.3.2 The readout electrode

The readout electrode is a multi-layer PCB of thickness 1.6 mm and size
316$\times$542 mm2, with an active area of 256$\times$512 mm2. The top layer
corresponds to the chamber readout strips. For $\phi$ views, there are 256
straight strips of length $512$ mm along the long dimension of the PCB ($z$)
and with a pitch of 1 mm and an interstrip distance of 100 $\mu$m. For the $z$
views, there are 256 strips of length $256$ mm along the short dimension of
the PCB ($\phi$) and with a pitch of 2 mm and an interstrip distance of 500
$\mu$m along $\phi$. A zigzag pattern is chosen for the $z$ view strips in
order to increase the electric charge sharing between neighbor strips and thus
the cluster size in the chamber, despite the large pitch. The other layers of
the PCB are used for routing the readout strips to 4 MEC8 connectors of 64
(active) + 6 (ground) pads each, located on the side of the PCB (Figure 6). A
ground layer is also added to the PCB layout to provide appropriate
electromagnetic shielding to the chamber.

Figure 6: Layout of the readout PCB for the $\phi$ (left) and the $z$ (left)
view. Shown is the layer in which readout strips are routed to 4 MEC8
connectors located at the bottom.

####  2.3.3 The resistive layer

The resistive layer has several purposes: protection against electromagnetic
discharges, protection of the readout electronics, spread of the charge, and
rate limitation. Here the main concern is caused by the many highly ionizing
particles created in heavy ion collisions. Early estimates of the charge
deposited in the drift volume of the chamber and assuming a ${\rm Au+Au}$
collision rate of 50 kHz gave a 100 Hz rate for crossing the Raether limit [5]
and likely causing discharges. Hence, the resistive layer was deemed necessary
to protect the SAMPA electronics. At 50 kHz collision rate, the resistive
layer must evacuate the electric charges quickly, which forbids the use of
Diamond-Like Carbon (DLC) [6] as a resistive layer. With low resistivity comes
large cluster sizes. To prevent this, the layer is segmented in the same
direction as the readout strips. A number of pitch values and patterns have
been tested for the resistive layer segmentation, illustrated in Figure 7. The
one with the smallest pitch (500 $\mu$m) was found to lead to the best
(smallest) spatial resolution and was adopted for both views.

Figure 7: Examples of resistive layer patterns tested on a 2 mm straight strip
readout pattern. From left to right : (i) 2 mm pitch and 1 mm straight strip;
(ii) 1 mm pitch and 600 $\mu$m straight strip; (iii) 500 $\mu$m pitch and 290
$\mu$m straight strip and (iv) 2 mm zigzag strips.

####  2.3.4 Fabrication

The fabrication process of a typical bulk Micromegas chamber is described in
[7]. For TPOT additional steps are needed to include the resistive layer
(Section 2.3.3). The resistive layer itself is fabricated using a thin layer
of isolating material (Kapton) on which the strip pattern is printed using
serigraphy. The readout PCB (Section 2.3.2) and the resistive layer are then
glued and pressed together. The gluing and pressing is performed at CERN’s
Micro-Pattern Gaseous Detector (MPGD) laboratory. The resulting assembly,
several layers of photoresistive film (Vacrel) of the appropriate thickness
and the woven wire micromesh are then laminated together at high temperature.
The photoresistive material is subsequently etched using photolitography to
produce the pillars that keep the micromesh at the proper distance (128
$\mu$m) from the resistive layer (Figure 3). These steps are performed in the
MPGD laboratory at the CEA research center of Saclay. In parallel, the carbon
fiber supported drift electrode is mounted on the 3D-printed frame which
defines the chamber active volume and provides gas circulation. Both are
assembled to the Micromegas bulk, using a set of screws and an O-ring seal
between the bulk and the printed frame to ensure gas tightness.

####  2.3.5 Conditioning

The conditioning of the Micromegas chambers consists of incrementally raising
the voltage of the resistive layer and the drift electrode, each time
monitoring the current drawn on the corresponding channel and waiting for it
to stabilize to a small, near-zero value before raising to the next step. This
procedure is performed at multiple stages of the chamber fabrication and
before installation.

At Saclay, the Micromegas bulk-assembly (readout plane, resistive layer and
micromesh, Figure 3) is first conditioned in air and without a drift
electrode, to a voltage up to 800 V. HV boards (Section 2.3, Figure 4) are
tested up to 1000 V for approximately twelve hours. The drift electrodes are
conditioned independently up to 1500 V. This conditioning is repeated once the
chamber is assembled and the gas tightness is verified, using the nominal
95/5% Ar/iC4H10 gas mixture, up to a value of 450 V for the resistive layer,
and 500 V for the drift electrode. This corresponds to a detector
amplification gain of approximately $5\times 10^{4}$, a value significantly
larger than needed for routine data taking in sPHENIX (Sec. 4.3.2). Gas
tightness is checked at 2 l/h gas flow and a leak rate tolerance of 0.02 l/h.

Upon reception at BNL, the same conditioning of the assembled modules is
repeated, to ensure that no damage occurred during transportation.

###  2.4 Mechanical structure

Figure 8 shows a schematic view of a TPOT module, with electronic boards and
cooling plates mounted. Each module consists of two Micromegas chambers (one
$\phi$ and one $z$ view) mounted back to back, two FEE boards (one per
chamber) and two water cooled aluminum plates.

Figure 8: Schematics of a fully assembled TPOT module. Figure 9: Picture of
the central TPOT sector, fully assembled, with four Micromegas modules,
readout electronics, cooling plates and services.

Figure 9 shows a picture of one fully assembled TPOT sector, out of three
sectors total. The modules are supported on 80/20 10 series aluminum beams
that form the frame for each of the three sectors. These are attached to cable
trays that house the HV cables, low voltage cables, optical fibers as well as
cooling and gas tubing, relevant for TPOT services (Section 2.6). Aluminum
plates are mounted at each end of the sector to support the extra cable, tube
and fiber lengths between the end of the cable tray and the patch panel to
which they are connected. The three sectors are held together at a
$150\degree$ angle by a total of fourteen trapezoidal connector brackets.

Each sector has mounting systems at both ends that are connected to aluminum
frames attached to the IHCAL. These enable precision movement in three
directions during and after installation, for precise positioning of the
detector. They consist of steel turnbuckles with ball joint ends as well as
swivel tip set screws.

To minimize deflection, adjustable support brackets are added to the midpoint
of each sector, which rest on the EMCAL. They consist of soft pads, an
aluminum frame, as well as steel swivel tip set screws for precision leveling.

###  2.5 Electronics

####  2.5.1 Readout system architecture

Figure 10: Block diagram of the sPHENIX readout system for TPOT.

Figure 10 shows the block diagram of the readout system for TPOT. The signal
from TPOT is fed into the FEE which amplifies, shapes and digitizes it into
data. Digital data is sent to the back-end electronics which consists of a
Data Aggregation Module (DAM) installed inside the Event Buffering and Data
Compressor (EBDC). The DAM is a specialized version of the FELIX board
developed for the ATLAS experiment at the Large Hadron Collider (LHC) [8]. The
EBDC is a high-end rack-mounted commodity server. Compressed data from the
EBDC is sent to Buffer Boxes, which are high capacity, high input-output
disks, and serve as a buffer before it is transferred to the Scientific Data
and Computing Center (SDCC) at BNL and recorded on permanent storage (tapes).

The FEE has an optical transceiver with four Tx/Rx channels (Quad Small Form
factor Plugable, QSFP). Two channels are dedicated to data transfer and
clock/trigger distribution and one channel is dedicated to the JTAG (Joint
Test Action Group) interface implemented over optical communication. The
optical fibers from the FEE are sorted out in the Sort-Out-Box (SOB) in which
the JTAG controller board called Mighty-JACK is installed. The TPOT readout
system uses 16 FEE, one SOB/Mighty-Jack, one DAM, and one EBDC.

####  2.5.2 The 32-channel SAMPA ASIC

The ASIC used for the FEE is called SAMPA v5 [9, 10]. Primarily designed for
the ALICE TPC and Muon Chamber upgrades [11, 12], the ASIC was modified for
the sPHENIX TPC requirement, the main modification consisting of including a
shaping time of 80 ns in place of 320 ns. A description and block diagram of
the ASIC is found in [9].

For TPOT, the SAMPA operation parameters are: (i) 160 ns shaping time, which
is the longest available value, chosen to increase the collection of the ion
tail in the Micromegas; (ii) 20 mV/fC gain, which is the smallest available
gain, chosen to minimize the fraction of signal that would saturate the ADC
range and (iii) 50 MHz sampling rate.

The dynamic range of the SAMPA ADC is 1024 units, with a conversion factor of
2.15 mV/ADU (ADC unit). Thus the full range of the ADC corresponds to 2.2 V,
or 110 fC at a nominal gain of 20 mV/fC.

The quoted gain of 20 mV/fC is measured assuming a small input capacitance
from the detector of order 10-20 pF. The effective gain becomes smaller when
the input capacitance increases. The input capacitance from the Micromegas
chambers, measured at Saclay, is $\sim 150$ pF ($\sim 300$ pF) for the $\phi$
($z$) view, respectively. The gain of the SAMPA ASIC for such capacitance
drops to 16 mV/fC ($13.5$ mV/fC), corresponding to 82% (68%) of the nominal
gain.

####  2.5.3 The 256-channel Front-End Electronics (FEE) Board

Figure 11: Circuit diagram of the TPOT FEE board. Figure 12: Picture of one
TPOT FEE board.

Each FEE board has eight SAMPA v5 chips that accept 256 inputs from the
detector. The block diagram of the FEE board is shown in Figure 11. A picture
of the board is shown in Figure 12. The two SAMTEC SEAF connectors on the
right collect the signal. They connect to the Micromegas chamber strips using
a dedicated transition cable, shown in blue in Figure 8. Connectors on the
right, top-to-bottom, correspond to the optical transceiver, low voltage
input, and a JTAG interface for board configuration redundant with the
interface implemented over the optical link. A Xilinx Artix-7 200T FPGA
(XC7A200T) mounted on the FEE has multiple functions: it collects data from
the SAMPA chips, formats them into a data packet, and sends the packet off to
the back-end electronics (DAM/EBDC) via the optical transceiver.

####  2.5.4 Magnetic field and radiation tolerance

The FEE boards are mounted directly on the TPOT modules (Figure 8) and sit
inside sPHENIX longitudinal $1.4$ T magnetic field. This field might affect
the performance of the optical transceiver, due to the presence of inductor
coils. During preliminary tests performed at the Massachusetts Institute of
Technology (MIT) and BNL Alternating Gradient Synchrotron (AGS) magnetic field
facilities, the FEE was placed in magnetic fields oriented along three
orthogonal directions and the transmission capability of the optical
transceiver was quantified using the optical connection eye-diagram. A
variation of $\sim 25$% in transmission capability was measured, acceptable
for optical communication.

Radiation tolerance for the FEE is another key factor for validating its
design and selection of individual electronic parts. The same FEE is used for
TPOT and the TPC. For the TPC, some boards are installed close to the beam
pipe, where the radiation dose is maximum. The Total Ionization Dose (TID) at
a 16 cm distance from the beam pipe and for 5 years of sPHENIX data taking is
estimated to be 25 kRad, using data from the PHENIX experiment [13]. It is
significantly smaller at the TPOT location. TID tests performed on the optical
transceiver and the full FEE board using a 60Co $\gamma$ source show that all
components of the FEE can sustain a 100 kRad dose except the EEPROM (42 kRad)
and the Phase-Lock Loop (PLL, $\sim 50$ kRad). These limits are well above the
dose expected during sPHENIX running.

####  2.5.5 Mighty-Jack and Sort-out-Box

The FEE board implements the JTAG control functionality over optical
communication so that (i) the FPGA EEPROM can be reprogrammed as needed and
(ii) the FPGA configuration memory can be monitored continuously and repaired
in case of damage (e.g. single event upsets). The Mighty-JACK board and the
fiber Sort-Out-Box (SOB) were developed to utilize this functionality.
Pictures of those are shown in Figure 13. The fiber configuration is shown in
Figure 14.

Figure 13: Left: Picture of the Mighty-JACK board, right: picture of the Sort-
Out-Box (SOB). Figure 14: Fiber configuration in the Sort-out-box.

####  2.5.6 The back-end electronics (DAM and EBDC)

Figure 15: Picture of the DAM board. Figure 16: Block diagram for DAM and
EBDC.

The FELIX board was developed primarily for the ATLAS experiment at CERN [8],
with the help of the BNL Instrumentation Division and the ATLAS group at BNL.
It satisfies all the requirements for interfacing with the SAMPA FEE. A
picture of the DAM board is shown in Figure 15, and Figure 16 shows the
corresponding block diagram, both using ATLAS FELIX v2.1 card.

A Xilinx Kintex Ultrascale XCKU115-2FLVF1924E FPGA is on board of each DAM. To
the FEE side, the FPGA is linked with four pairs of Mini Parallel Optical
Device (MiniPOD) with 12 channels each. Each of the 48 pairs of fiber link
supports up to 12.8 Gb/s bi-directional data rate. The DAM FPGA is also linked
to the EBDC server using a 16-lane PCI Express Gen3 connection which supports
more than 100 Gb/s. For sPHENIX, one of the 48 fiber links is redirected to a
clock/trigger mezzanine module that provides an optical connection with
sPHENIX Global Timing Module (GTM) using an SFP+ (Small Form factor Plugable)
transceiver.

The raw data is buffered in the DAM for up to 20 $\mu$s. Only the data that
falls within an adjustable time window matching a given trigger is sent for
further processing via a throttling algorithm in the DAM FPGA. For the TPC,
this reduces the data volume by about a factor four. A significantly larger
reduction is expected for TPOT. It is possible to implement some clustering of
the data on the DAM FPGA for additional data reduction. After transmitting the
data to the EBDC, lossless compression is performed on the CPU before sending
it out to sPHENIX DAQ for further processing and storage.

###  2.6 Services

####  2.6.1 High voltage

The High Voltage (HV) system of TPOT consists of one negative drift electrode
and four positive resistive electrodes for each chamber, for a total of eighty
HV channels. HV is supplied by CAEN HV units A7030SN and A7030SP through
appropriate Safe High Voltage (SHV) cables. Each channel can deliver voltage
of up to 3 kV and currents up to 5 mA, well above the typical operation range
necessary for Micromegas (voltage less than 1 kV and currents of at most 10
$\mu$A). A total of six 24-channel HV units are used (two negative and four
positive), controlled by CAEN SY4527 universal multichannel power supply
system.

To protect the chambers from discharge-induced damage, TPOT is equipped with a
spark protection system through the HV units. If a given channel draws more
current than an adjustable limit (typically a few $\mu$A, channel-dependent),
for more than a given adjustable amount of time (typically a few seconds), the
voltage is ramped down safely, and the channel is turned off.

TPOT HV is operated with three modes: OFF, SAFE and ON. When in the OFF mode,
the channels all hold 0 V. When in SAFE mode, the channels hold low enough
voltage that there is no amplification in the chambers. SAFE mode is typically
used when there is no stable beam in the accelerator. When in ON mode, the
chambers are all brought up to operating voltage to collect data from
collisions with optimal gain and stability.

####  2.6.2 Low voltage

The same power system provides the Low Voltage (LV) for the TPC and TPOT FEE.
Three LV channels are needed to power the FEE: $0.5$ A at $4$ V (digital),
$2.4$ A at $2$ V (digital) and $2.4$ A at $2$ V (analog), corresponding to a
total power consumption of about 12 W per FEE board and 192 W total, for TPOT.

Figure 17:  Low voltage power distribution scheme for TPC/TPOT FEE.

Figure 17 shows the design of the low voltage power distribution scheme for
the TPC/TPOT FEE. A Vicor MegaPak 4 kW is used for the bulk power supply, in
which ten 400 W DC-DC converters are installed. 5 V and 4 V modules that
supply up to 80 A are used. Considering the significant voltage drop between
power distribution boards and FEE, one 5 V module is used to provide the 4 V
line, and two 4 V modules for each 2 V lines. The distribution board is
designed so that one board powers up to 20 FEEs. TPOT uses 12 channels of one
distribution board (4 for each TPOT sector).

####  2.6.3 Cooling

Figure 18: Left: design of the FEE cooling plates; right: picture of one
assembled cooling plate.

The FEE boards are cooled using a sub-atmospheric pressure cold water circuit.
The water is circulated through aluminum cooling plates mounted on top of each
board and separated by a thermally conductive gap-pad material. A diagram of
the cooling plate and a picture of the assembled plate are shown in Figure 18.
A Childyne unit is used to circulate the sub-atmospheric pressure water. Water
is provided to each module independently and each line is monitored by its
individual flowmeter. The cooling plates of the two FEE boards of a given
module are mounted in series. Using sub-atmospheric pressure water ensures
that in the event of a leak, air leaks inward but no liquid leaks outward.
With this setup, the cooling water input temperature set to 21$\degree$C and
the cooling water flow rate to 125c̃cm, the temperature measured on the FPGA
of the FEE boards is around 40$\degree$C.

####  2.6.4 Gas system

Figure 19: Schematics of the TPOT gas system.

Figure 19 shows the schematics of the TPOT gas system. The two chambers in
each module are connected in series. Each module has a volume of $1.4$ l for a
total of $11.2$ l. The gas flow is operated in the 50-100 ccm range. Each
module is equipped with its own flowmeter.

TPOT uses premixed gas bottles of 95/5% Ar/iC4H10. Each of the eight modules
has its own input and output lines that connect to the patch panel, which in
turn all connect in parallel to the same gas bottle system. The gas bottle
system supplying the gas mixture to the chambers is made up of two 12-bottle
packs which switch seamlessly to ensure a continuous and uniform flow. The gas
system operates at a pressure slightly above atmospheric. Six modules maintain
proper flow at approximately 1 inch H2O over-pressure. The remaining two
modules operate at higher over-pressures of 4 and 6 inch H2O due to higher
internal resistance.

For safety, three more tubes are added to TPOT that connect to the middle of
each sector and allow to (i) detect any build-up of gas between TPOT and EMCAL
in case of a leak and (ii) inject nitrogen to displace the built-up gas.

###  2.7 Grounding

Figure 20: Schematics of the TPOT grounding scheme.

A simplified description of the TPOT grounding is shown in Figure 20. Two
separate grounds are considered: the infrastructure ground to which all
metallic structures of all sPHENIX subsystems connect and the instrumentation
ground, which coincides with the return lines of the HV and LV power supply.
For TPOT the infrastructure ground connects the support structure, the FEE
support plates and the FEE cooling plates. The instrumentation ground connects
the LV and HV return lines, the FEE ground, the Micromegas readout PCB ground
and the micromesh. By default the infrastructure and instrumentation ground
are connected at the sPHENIX earth, located on the main magnet support. In the
TPOT case, electromagnetic noise is found to be minimum if another connection
between the two grounds is added at the detector patch panel.

###  2.8 Slow Control and monitoring

The TPOT slow control and monitoring system tracks a number of measurements
relevant to detector operation using a Grafana web interface and the time
series database Prometheus [14]. This includes, but is not limited to: (i) the
voltage and current of each HV channel, (ii) the voltage and current of each
LV channel, (iii) the log and history of tripped HV channels, (iv) the
temperature of the FEE and (v) the recorded data rate. Five temperatures per
FEE are measured to monitor the FPGA as well as the FEE PCB. The LV power
supply is interlocked to the sub-atmospheric cooling water system, so that it
is not possible to turn ON the FEE in absence of cooling. An example of the
monitoring of HV and current values over time is shown in Figure 21.

Figure 21: Example of monitoring of the HV (left) and current (right) of two
TPOT chambers over time. The low and high values on the HV panel correspond to
TPOT being in either SAFE or ON state, respectively, depending on the presence
of beam in sPHENIX.

##  3 Detector Installation

###  3.1 Installation Mechanics

Each of the three TPOT sectors was assembled separately at BNL and later moved
on a transportation cradle (Figure 22, left), used to bring the detector to
the sPHENIX experimental hall.

Figure 22: Schematics of the TPOT detector, transportation cradle, and
installation mechanics.

In the experimental hall, the detector was attached to a suspended aluminum
I-beam, which became a part of both the lifting and insertion mechanism. This
I-beam was then attached to a preexisting fixture in the magnet bore and
connected in series with two other beams to allow for the longitudinal
translation of the TPOT detector into its final $z$ position (Figure 22,
right).

Figure 23: Pictures of the TPOT installation inside the sPHENIX EMCAL.

After translation, TPOT was lowered and aligned to the EMCAL using turnbuckles
as a part of the lifting beam (Figure 22, right). It was then secured through
its end brackets to hollow EMCAL end frames that rest on the IHCAL. The mid-
length support was preset while on the cradle to align with the EMCAL.

Figure 23 shows two pictures of the TPOT installation inside the EMCAL.

During installation, one of the 32 signal transition cables that runs between
the detector and its corresponding FEE (Section 2.5.3 and Figure 8) got
disconnected from the board. After installation, it was not possible to access
and reconnect this cable. This corresponds to an acceptance loss of half a
chamber, or 3%.

###  3.2 Metrology and Survey

Metrology and survey aim to provide the position of the Micromegas strips in
the sPHENIX reference frame, after TPOT installation. This is achieved in
three steps: (i) after fabrication and assembly of each Micromegas module, one
measures the position of the strips with respect to a set of reference targets
on the module; (ii) after assembly of the modules on their support structure,
one measures their position with respect to a set of targets mounted on the
structure and (iii) after installation of the TPOT detector inside the EMCAL,
one measures the position of its support structure in the sPHENIX reference
frame.

The first step is referred to as metrology. Each module is surrounded by a
tray screwed into the module frame. On the top side there are three space
points to insert optical targets into pins for metrology. The pins are either
inserted into drill-bushed holes on the detector frame or glued directly on
the frame. Measurements of the relative position of the detector strips to the
pins were performed at Saclay. First one uses the readout PCB drawings to
determine the position of the strips with respect to a set of fixed reference
points on the PCB (here, the edges of the MEC8 connectors), then one measures
the position of these reference points with respect to the optical targets.

The second and third steps rely on optical targets inserted into drill-bushed
holes inside the support structure, at each end of each TPOT sector. The
measurements were performed at BNL by the local survey team.

The combination of these three sets of measurements allows the position of the
strips inside sPHENIX to be known at a precision of a few 100 $\mu$m. This
determination is then improved using the reconstructed trajectory of particles
passing through sPHENIX tracking detectors, to an accuracy of 100 $\mu$m or
less. Millepede [15] is used to perform this track-based alignment.

##  4 Detector Characterization and Performance

###  4.1 Detector characterization at Saclay

Figure 24 shows a picture of the cosmic test bench used at Saclay for the
characterization of the TPOT modules during production. The modules are
positioned in the middle trays of the setup between two sets of MultiGen
detectors [16] (two detectors above and two detectors below), used for the
reconstruction of the reference track. 3D-printed indentation fixtures are
attached to the cosmic test bench so all the modules are tested at the same
position with respect to the reference detectors. Data acquisition is
triggered using a coincidence of scintillating pads mounted on Photo-
Multiplier Tubes (PMT). The signal is read with the DREAM electronics [17]
originally developed for the Micromegas detector of the CLAS12 experiment at
the Jefferson Laboratory [18, 19].

Figure 24: Picture of the cosmic test bench used at Saclay for the
characterisation of the TPOT modules during production.

Figure 25: Detection efficiency as a function of position in two chambers of
the same TPOT module. Left: $\phi$ view; right: $z$ view.

The detection efficiency measured as a function of position in the two
chambers of a given TPOT module using this setup is shown in Figure 25. The
efficiency is uniform in the entire active area of the two chambers, and above
90% everywhere.

Figure 26: Detection efficiency as a function of resistive layer HV, left: for
the two chambers of the same TPOT module, using DREAM electronics at Saclay;
right: for the $z$ view of a given TPOT module, measured at BNL using SAMPA.

Figure 26 (left) shows the detection efficiency measured as a function of the
HV applied to the resistive layer in the two chambers of a given TPOT module.
Efficiency increases with HV and full efficiency is reached at about 400 V for
both chambers.

Figure 27: Distribution of the residuals (cluster-reference) in two chambers
of the same TPOT module. Left: $\phi$ view; right: $z$ view.

Figure 27 shows the residuals measured in the two chambers of a given TPOT
module, obtained by comparing the position of the measured cluster to that of
the track provided by the reference detectors of the cosmic test bench. The
width of the residuals distribution is the quadratic sum of the chamber
intrinsic spatial resolution and the accuracy of the reference track. It is an
upper limit to the chamber intrinsic resolution. The sum of two Gaussian
distributions is used to fit the signal. The width of the narrow Gaussian
distribution is 390 $\mu$m (500 $\mu$m) for the $\phi$ ($z$) view,
respectively. The larger value measured for the $z$ view is due to its larger
pitch. The wide Gaussian distribution is used to describe the tails of the
residuals distribution. It has a width of about 1.5 mm for both views. These
tails are attributed in parts to (i) the contamination from poorly defined
reference cosmic tracks, (ii) the contamination from poorly defined clusters
in the detector, and (iii) the presence of the pillars that support the
Micromegas mesh at a fixed distance above the resistive layer and distort the
electric field locally.

###  4.2 Detector characterization at BNL

####  4.2.1 Detection efficiency with SAMPA electronics

The detector performance presented in section 4.1 was measured using the data
acquisition system available at Saclay, and in particular the DREAM ASIC [17].
Compared to SAMPA, primarily designed for low-capacitance detectors (a few 10
pF), DREAM is better suited to the typical capacitance of Micromegas detectors
(up to 300 pF in the TPOT case) leading to improved signal-to-noise ratio,
smaller signal detection threshold and a higher detection efficiency at a
given amplification gain.

Figure 28: Picture of cosmic test bench used at BNL for the characterisation
of the first TPOT module equipped with the SAMPA electronics.

Figure 26 (right) shows the detection efficiency measured as a function of the
resistive layer HV in the $z$ view of the first TPOT module received at BNL
and equipped with the SAMPA electronics. This measurement is performed using a
simplified cosmic test bench available at BNL and shown in Figure 28.
Reference cosmic tracks are triggered on using a coincidence of four large
scintillating pads mounted on PMTs, to define the region of interest in the
detector, one of which is located above the detector and the other three
below. Efficiency is defined as the fraction of triggers for which at least
one hit above threshold is measured in the detector. The threshold is set to
five times the equivalent noise charge (ENC) above pedestal. Compared to
figure 26 (left), the detection efficiency measured at a given voltage with
SAMPA is systematically lower than that measured with DREAM. Full efficiency
is reached for voltages larger than 420 V, as opposed to 400 V with DREAM.

###  4.3 Detector commissioning with beam and cosmic rays

####  4.3.1 Noise Levels and Timing

Noise levels and pedestals measured post-installation inside sPHENIX are
presented in Figure 29, on the left as a function of strip number and on the
right, integrated over all strips. Noise levels are uniform across all 4096
TPOT strips, except for the region around strip 2100 where the electronics is
not connected to the detector (section 3.1) and for which the input
capacitance is nearly zero. Excluding this region, the Root Mean Square (RMS)
of the noise levels is about 8 ADC counts, corresponding to $7\times 10^{3}$
($8\times 10^{3}$) electrons ENC for the $\phi$ $(z)$ views, respectively, and
using the effective gains quoted in section 2.5.2. For comparison, the typical
ENC achieved with DREAM amounts to $6\times 10^{3}$ electrons.

Figure 29: Left: Pedestal and noise levels measured in all TPOT channels
during data taking. Right: pedestal-corrected noise count distribution. The
RMS of the distribution is approximately 8 ADC counts. Figure 30: Signal time
for particles passing through one TPOT chamber measured with respect to
trigger time.

The timing of the signal with respect to the trigger is verified by counting
the number of hits above threshold as a function of time after trigger. The
resulting distribution is shown in Figure 30. It is measured using
${\sqrt{s_{\rm NN}}}=200$ GeV ${\rm Au+Au}$ collisions at RHIC, MBD for
triggering111The MBD trigger requires the coincidence of at least one hit in
each of the two MBD sections on either side of the interaction point. and with
the TPOT detector operated at nominal voltage (Section 4.3.2). The majority of
signal hits are well contained between samples 20 and 40, corresponding to a
time window of 1 $\mu$s.

####  4.3.2 Operation Point Determination

Without magnetic field: a first estimate of the post-installation detection
efficiency of the Micromegas chambers is obtained by using the correlation
between the number of clusters measured in the two views of the same module.
In absence of magnetic field and for a drift HV of -100 V, full efficiency is
reached at a resistive layer HV of approximately 400 V. This is consistent
with the measurements discussed in Sections 4.1 and 4.2.

Figure 31: Signal timing distribution as a function of strip number for
different values of the drift electrode HV in both chambers of the same TPOT
module. Top two rows: $\phi$ view; bottom two rows: $z$ view.

With magnetic field: the sPHENIX magnetic field is perpendicular to the drift
direction of the primary electrons deposited by particles in the Micromegas
drift space. Consequently it curves the trajectory of the primary electrons in
the transverse plane, resulting in a non-zero value for what is known as the
Lorentz angle, which is the angle between the drift direction of the electrons
and the direction of the electric field. Too large values of the Lorentz angle
result in deteriorated spatial resolution and efficiency due to spread of
primary electrons. For a given magnetic field, the Lorentz angle is reduced by
increasing the drift electric field. This is achieved by increasing the drift
HV.

Figure 31 shows the distribution of the signal time as a function of the
position in the detector (labeled as strip number) for scanned values of the
drift electrode HV and in two TPOT chambers. The distributions are measured
with ${\rm Au+Au}$ collisions, MBD trigger and magnet on. For small absolute
values of the drift electrode HV (e.g. -50 V and -100 V) the timing
distributions are significantly wider than that shown in Figure 30 and the
width reaches 40 sample units (2 $\mu$s). In addition the distributions
exhibit a dependence on the position in the chamber for the $\phi$ view, not
observed for the $z$ view. This difference is attributed to the direction
along which the electron trajectory is bent with respect to that of the
measuring strips: for $\phi$ ($z$) views, the electron trajectory is bent
perpendicular to (along) the strip direction.

The width of the distributions decreases with increasing drift electrode HV
and the dependence on the position in the chamber vanishes. For values larger
(in magnitude) than -300 V, the distributions become as narrow as that
measured without magnetic field, indicating that the Lorentz angle becomes
small enough that it does not significantly alter the signal collection in the
chamber. With this study, it was decided to operate the drift electrodes at a
HV no smaller in magnitude than -300 V.

Figure 32: Estimate of the detection efficiency measured in two chambers of
the same TPOT module during data taking, as a function of the bias voltage.
The boxes around the points represent the systematic uncertainties. See text
for details.

Figure 32 shows an estimate of the detection efficiency measured in the two
chambers of the same TPOT module as a function of the resistive layer HV with
magnetic field on and a drift electrode HV of -400 V. The boxes around the
points represent the systematic uncertainties on the measurement, estimated by
varying the selection criteria of either the reference or the measured cluster
sample. They are largely correlated point-to-point. Full efficiency is reached
starting from approximately 410 V. The detection efficiency dependence on the
resistive layer voltage is similar to that shown in Figure 26 (right) and
measured with cosmic tracks. However, i) the efficiency measured on the
plateau is significantly lower (about $90$% as opposed to $100$% in Fig. 32
and ii) the efficiency does not drop to zero for small values of the voltage.
Both observations are attributed to the poor definition of the reference
sample. A more accurate determination of the reference sample and the
detection efficiency requires correlating the TPOT measurements to that of
other tracking subsystems, in particular the TPC.

####  4.3.3 Operation Mode Description

Based on the results presented in sections 4.1, 4.2 and 4.3.2, the values
chosen as nominal drift and resistive layer HV for detector operation during
data taking are: $V_{\rm drift}=-300$ V, $V_{\rm resist}=400$ V. This
corresponds to a detector amplification gain of about $10^{4}$. Out of the 64
resistive layer HV channels (four for each of the 16 TPOT chambers), 62 are
operated at the nominal value while the remaining two are operated at 360 V
and 380 V because they exhibit high current draw (several $\mu$A) and frequent
discharges for larger values. This corresponds to detection efficiencies of
about 50 and 70% respectively (Figure 32). Combining this information with the
one signal transition cable disconnected from the chamber during installation,
one reaches an overall acceptance for TPOT of about 96%, assuming 100%
efficiency on the plateau.

Figure 33: Distribution of the cluster size (top) and the number of signal
hits per ${\sqrt{s_{\rm NN}}}=200$ GeV ${\rm Au+Au}$ collision (bottom) for
$\phi$ (left) and $z$ views (right).

Figure 33 shows the cluster size distribution and the number of signal hits
per ${\rm Au+Au}$ collision in TPOT. The mean cluster size is 3.5 (2.6) for
$\phi$ ($z$) views. The larger size measured in the $\phi$ views is attributed
to the detector pitch being smaller and the azimuthal angular distribution of
the particles passing through the chamber being wider due to the presence of
the magnetic field. The mean occupancy, defined as the mean fraction of strips
with a signal above threshold in a given chamber and per ${\rm Au+Au}$
collision, is $9.3\%$ ($6.2$) for $\phi$ ($z$) views. The difference between
the two is attributed to the larger mean cluster size measured for $\phi$
views.

Figure 34: Distribution of the cluster position in the $\Phi$ (left) and $z$
views (right), without magnetic field (top) and with magnetic field (bottom).

Figure 34 shows the distributions of the cluster position in the $\phi$ and
$z$ views, both with and without magnetic field. For the $z$ views, the
distributions show similar $z$ dependence with and without magnetic field.
They exhibit a slight slope attributed to the fact that the center of the
vertex $z$ distribution is shifted with respect to the center of the TPOT
detector. On the contrary for the $\phi$ views, the distribution is flat
without magnetic field, but exhibits an approximately linear dependence on the
position with magnetic field. This dependence is attributed to the effect of
the magnetic field on the drift of the primary electrons in the chamber, which
occurs in the direction parallel to the measurement.

####  4.3.4 Correlation with Other Subsystems

Figure 35: Correlation between the total charge measured in sPHENIX MBD and
the total number of clusters measured in TPOT.

Figure 35 shows the correlation between the total charge measured in the MBD
and the total number of clusters measured in TPOT. The two quantities are
strongly correlated despite the fact that there is no overlap between the
acceptance of the two detectors (Figure 1). The correlation proves that the
signal measured in TPOT corresponds to particles created in the same ${\rm
Au+Au}$ collision as that recorded by the MBD. Events with a small number of
clusters and small charge in the MBD correspond to peripheral collisions for
which the distance between the center of the colliding nuclei is large.
Conversely, events with a large number of clusters and a large charge in the
MBD correspond to central collisions for which this distance between the two
centers is small.

Figure 36: Three examples of cosmic tracks reconstructed in sPHENIX MVX, INTT
and TPOT detectors.

Three examples of cosmic tracks reconstructed in MVTX, INTT and TPOT are shown
in Figure 36. The curvature of the trajectory is due to the presence of the
magnetic field. Successfully reconstructing these trajectories with the MVTX,
INTT and TPOT and extrapolating it inside the TPC is key to measuring and
correcting for the beam-induced distortions of the electron drift inside the
TPC.

##  5 Conclusion

In this paper, the Time Projection Chamber Outer Tracker (TPOT) installed in
the sPHENIX experiment is presented. It sits on the outside of the TPC and
provides an additional space point along the particle trajectory to better
constrain the tracks inside the TPC and help calibrate out the distortions of
the electron drift in the TPC volume. The detector design, installation,
characterization and performance are described. TPOT is fully commissioned and
ready to collect data for the upcoming RHIC runs. Using cosmic data it has
demonstrated the ability to provide reference trajectories to the TPC together
with the other tracking detectors.

## Acknowledgments

We are grateful to the sPHENIX Collaboration for their support and help during
the various phases of the TPOT project. We thank the Brookhaven National
Laboratory sPHENIX staff for their help during assembly and installation, and
in particular C. Biggs, J. Labounty, M. Lenz, S. Pollizo and F. Toldo. We also
thank the MBD, MVTX and INTT teams for providing the data appearing in Figures
35 and 36, as well as the staff of the Collider-Accelerator and Physics
Departments at Brookhaven National Laboratory. We acknowledge support from the
Office of Nuclear Physics in the Office of Science of the Department of
Energy, the National Science Foundation, the Commissariat à l’Énergie Atomique
(France), and Fundação de Amparo à Pesquisa do Estado de São Paulo (Brasil),
processo 2014-12664-3.

This manuscript has been authored by employees of Brookhaven Science
Associates, LLC under Contract DE-SC0012704 with the U.S. Department of
Energy. The publisher by accepting the manuscript for publication acknowledges
that the United States Government retains a non-exclusive, paid-up,
irrevocable, world-wide license to publish or reproduce the published form of
this manuscript, or allow others to do so, for United States Government
purposes.

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11institutetext: Fudan University
11email: {zqzhan14, 12301010048, chenjingjing, jieliu<EMAIL_ADDRESS>22institutetext: Inception Institute of Artificial Intelligence
22email<EMAIL_ADDRESS>33institutetext: Department of Digestive Diseases,
Huashan Hospital

# Colonoscopy Polyp Detection:
Domain Adaptation From Medical Report Images to Real-time Videos

Zhi-Qin Zhan 11 Huazhu Fu 22 Yan-Yao Yang 1133 Jingjing Chen 11 Jie Liu 1133
Yu-Gang Jiang 11

###### Abstract

Automatic colorectal polyp detection in colonoscopy video is a fundamental
task, which has received a lot of attention. Manually annotating polyp region
in a large scale video dataset is time-consuming and expensive, which limits
the development of deep learning techniques. A compromise is to train the
target model by using labeled images and infer on colonoscopy videos. However,
there are several issues between the image-based training and video-based
inference, including domain differences, lack of positive samples, and
temporal smoothness. To address these issues, we propose an Image-video-joint
polyp detection network (Ivy-Net) to address the domain gap between
colonoscopy images from historical medical reports and real-time videos. In
our Ivy-Net, a modified mixup is utilized to generate training data by
combining the positive images and negative video frames at the pixel level,
which could learn the domain adaptive representations and augment the positive
samples. Simultaneously, a temporal coherence regularization (TCR) is proposed
to introduce the smooth constraint on feature-level in adjacent frames and
improve polyp detection by unlabeled colonoscopy videos. For evaluation, a new
large colonoscopy polyp dataset is collected, which contains 3056 images from
historical medical reports of 889 positive patients and 7.5-hour videos of 69
patients (28 positive). The experiments on the collected dataset demonstrate
that our Ivy-Net achieves the state-of-the-art result on colonoscopy video.

###### Keywords:

Polyp detection Colonoscopy video Domain adaptation.

##  1 Introduction

Colonoscopy allows Colorectal Cancer (CRC), the third most common cancer
globally, to be preventable [4, 3]. Before threatening health, CRC performs as
colon polyps that could be detected and treated by colonoscopy at an early
stage [16, 6]. Thus precise polyp detection is essential. However, due to the
variety of colon polyps in morphology, distinguishing them from normal
structures is challenging. There is an ineluctable polyp miss rate even for
professional endoscopists [10, 18]. Computer-aided Detection (CADe) of colon
polyps is therefore in demand [1]. Moreover, different from other medical
imaging tasks such as CT and MRI, the observation of colonoscopy is closely
related to endoscopists’ operation. Fig. 1 shows a typical procedure of
finding a colon polyp, which could be summarized into four steps as: Step I)
Scan the colon for polyps; Step II) A polyp comes into view; Step III) The
endoscopist notices a potential polyp and stops withdrawing; Step IV) Adjust
the visual field to have a closer observation, and maybe capture photos for
the report. Endoscopists need to judge and act at Step III, in order to
observe closer and record at Step IV. Hence, CADe is most clinically needed at
Step II when polyps first come in view, i.e., in real-time video streams.

Figure 1: Procedure of Finding a Polyp. Modern colonoscopy inserts a long
flexible tube from rectum to ileum. That tube contains artificial illuminants,
cameras, and channels for air, water as well as devices. Endoscopists observe
through cameras during withdrawal of the tube. They can bend and rotate the
tube’s terminal to adjust the visual field. A typical procedure of finding a
polyp into four steps, and CADe is most helpful at Step II.

With the recent rise of deep learning, research on CADe of colon polyps has
flourished, both at image-level [19, 20, 2] and video-level [21, 23, 13, 9].
Wang et al. demonstrated the clinical feasibility of CADe via further
prospective trials [19, 20], with a CNN model trained with still colonoscopy
images. However, its video-level performance is not satisfactory [12]. Other
research also found the similar phenomenon in [17, 14]. We attribute this
performance drop to the semantic gap between Step IV training images and Step
II testing video frames (see Fig. 1). If so, models trained directly with Step
II videos work best theoretically. But in clinical practice, such video data
is much harder to obtain, especially for positive samples. Carefully captured
Step IV colonoscopy images are available from historical medical report
database, while videos need to be specially recorded from scratch. Sufficient
number of positive patients are difficult to find in a short time. Lack of
positive samples may cause the model to overfit small datasets and hard to
generalize. Besides, video annotation is much more costly than images. It is
difficult to collect a large scale video dataset with high quality annotation
for deep learning techniques. In summary, training model with images has the
domain gap between the image and video, simultaneously, training model with
videos will suffer from the limitation of video data size. Thus, addressing
the domain gap between colonoscopy images from historical medical reports and
real-time videos is an effective solution.

In this paper, we consider joint utilization of colonoscopy images and videos
to alleviate the dilemma above. First, we defined an image-video-joint
colonoscopy polyp detection task, which aims to train the model by using
labeled images and negative video frames and infer on the target colonoscopy
videos. Second, we propose an Image-video-joint polyp detection network (Ivy-
Net) to address the domain gap between colonoscopy images from historical
medical reports and real-time videos. In our Ivy-Net, a modified mixup is
utilized to generate training data by randomly combining the images and
negative video frames at the pixel level. Third, we further exploit the
temporal information of videos, by using temporal coherence regularization
(TCR) on feature-level during model training. Fourth, we collect a large
colonoscopy dataset that combines 3056 images from historical medical reports
of 889 positive patients (diagnosed as colon polyp) and 7.5-hour colonoscopy
videos of 69 patients (28 positive). All images are annotated with bounding
boxes; all videos are provided with time slice labels of positive or negative;
in total 5050 of positive video frames are annotated with bounding boxes.
Moreover, the experiments on our collected dataset demonstrate that our Ivy-
Net achieves the state-of-the-art result, and improves the average precision
(IoU = 0.5) of polyp detection on colonoscopy video from 0.631 to 0.717. All
the codes and dataset will be released upon publication of this work.

Figure 2: The flowchart of our Ivy-Net. (a) We create the virtual input
$\tilde{x_{i}}$ by mixing up the input image with a negative video frame at
the pixel level, i.e., sum by random weight $\lambda$, without changing its
bounding box label $\tilde{y_{i}}$ (Section 2.1). (b) $f_{j-1},f_{j},f_{j+1}$
are features of adjacent video frames $x_{j-1},x_{j},x_{j+1}$, encoded by the
Encoder of polyp detection model. $\hat{f_{j}}$ is estimation of $f_{j}$,
calculated by $f_{j-1}$ and $f_{j+1}$, e.g. arithmetic mean here.
Regularization Loss is calculated from $\hat{f_{j}}$ and $f_{j}$ by the loss
function $L_{reg}$.

##  2 The Proposed Approach

In this paper, we propose an Image-video-joint polyp detection network (Ivy-
Net) to address the domain gap between colonoscopy images from historical
medical reports and real-time videos. By leveraging a large number of
unlabeled or weakly labeled videos, our Ivy-Net improves the performance of
colonoscopy polyp detection from two aspects. Firstly, we want to utilize the
semantic information in video frames (see Fig. 2-a). We augment the labeled
images, constructing virtual samples by mixing them up with negative video
frames during model training. Secondly, we exploit the video time series
information (see Fig. 2-b). We use the coherence between adjacent frames to
regularize model training. The following parts of this section will detail
each of these two approaches.

Although compared with the two above, post-processing which combines detection
results from neighboring frames is a more obvious approach. While in this
work, we intend to explore the potential of single-frame detector itself
instead. Our approaches can be used together with post-processing techniques
if necessary.

###  2.1 Mixing up With Negative Frames

Mixup, a data augmentation technique, was first introduced by [22, 8], applied
on image classification tasks. According to the definition in [22], it
constructs virtual samples $(\tilde{x},\tilde{y})$

$\tilde{x}=\lambda x_{i}+(1-\lambda)x_{j}$ $\tilde{y}=\lambda
y_{i}+(1-\lambda)y_{j}$ (1)

where $(x_{i},y_{i})$ and $(x_{j},y_{j})$ are two arbitrary samples from
training data; $x_{i}$ and $x_{j}$ are input images; $y_{i}$ and $y_{j}$ are
one-hot encoded classification labels; $\lambda$ is a random variable in the
interval $[0,1]$, controlling the mixup ratio.

####  2.1.1 Mixup Labels

Bounding box labels in image detection tasks are hard to encode as in (1).
[24] simply stacks all bounding boxes from two training images equally.
However in our experiments, the detection precision drops with bounding boxes
stacked. Therefore, we choose to mixup positive image samples with negative
video frames to avoid this problem. We use the image’s bounding box label as
the virtual sample’s bounding box directly.

$\tilde{y}=y_{image_{i}}$ (2)

####  2.1.2 Mixup Inputs

Since the images and video frames all have the same aspect ratio, we simply
resize the video frames to the size of images before mixup.

$\tilde{x}=\lambda x_{image_{i}}+(1-\lambda)x_{frame_{j}}$

In our experiments, $\lambda$ ’s distribution is also crucial to the detection
precision. We tested two design choices of it. The first one follows an
asymmetric Beta distribution that gives more weight to labeled images:

$\lambda\sim Beta(\alpha,\alpha+1)$ (3)

where $\alpha$ is a constant hyperparameter. Larger $\alpha$ will give higher
average weight to labeled images in our virtual samples. We define the second
discrete probability distribution as follows,

$\lambda=cX$ (4) $Pr(X=1)=p,X\in\\{0,1\\}$

where X is a random variable that can be 0 or 1, with the possibility of $p$
being $1$; $p$ and $c$ are two constant hyperparameters.

###  2.2 Temporal Coherence Regularization of Adjacent Frames

We assume that at high frame rate sampling (e.g. 60fps in our experiments),
adjacent frames of a video contain very similar semantic information. Hence
they should also have similar features after encoding with neural networks.
This similarity provides a kind of supervision that can be used as
regularization during training, forcing the encoder to be smooth in the time
dimension.

With this similarity, we further assume the local linearity in the time
dimension. Formally, with encoder $G$ and parameters $\theta$, we get the
feature $f$ from the input $x$.

$f=G(\theta,x)$ (5)

$f_{j-1},f_{j},f_{j+1}$ are features of arbitrary adjacent frames
$x_{j-1},x_{j},x_{j+1}$. We define the estimation of $f_{j}$ as the arithmetic
mean of $f_{j-1}$ and $f_{j+1}$.

$\hat{f_{j}}=\frac{f_{j-1}+f_{j+1}}{2}$

With the local linearity assumption, $\hat{f_{j}}$ should approach $f_{j}$
when the frame rate approaches infinity. We use the cosine distance to measure
the similarity of $f_{1}$ and $\hat{f_{1}}$. We calculate the regularization
loss $L_{reg}$ at each point of the feature map, and then take the average
over width and height dimensions.

$L_{reg}(\theta,x_{j-1},x_{j},x_{j+1})=1-cos(f_{j},\hat{f_{j}})=1-\frac{f_{j}\cdot\hat{f_{j}}}{\|f_{j}\|\|\hat{f_{j}}\|}$

The overall regularized detection loss $L_{reg\\_det}$ is the weighted sum of
the detection loss $L_{det}$ and the regularization loss $L_{reg}$,

$L_{reg\\_det}(\theta,x_{i},y_{i},x_{j-1},x_{j},x_{j+1})=L_{det}(\theta,x_{i},y_{i})+\gamma
L_{reg}(\theta,x_{j-1},x_{j},x_{j+1})$

where $(x_{i},y_{i})$ is an arbitrary labeled image training sample, and the
weight $\gamma$ is a hyperparameter.

##  3 Experiments

We will first introduce our datasets and evaluation metrics to further clarify
our newly proposed image-video-joint colonoscopy polyp detection task. Then we
will introduce our Image-video-joint polyp detection network (Ivy-Net) and
training settings. Finally, we will evaluate each of our approaches
respectively.

Figure 3: Demo Detection Results on Collected Colonoscopy Dataset. (a) and (b)
are video frames of two polyps.

###  3.1 Datasets and Evaluation Metrics

####  3.1.1 Datasets

Our colonoscopy dataset contains 3056 images from historical medical reports
of 889 positive patients (diagnosed as colon polyp) and 7.5-hour videos
(60fps) of 69 patients (28 positive) recorded at *** Hospital. By professional
endoscopists in our team, all images are carefully annotated with bounding
boxes; all videos are annotated with time slice labels of positive or
negative; 5050 of positive video frames (12fps sampled) are annotated with
bounding boxes.

Data Collection and Annotation: All images were captured by Fujinon 4400/4450
systems (Fujifilm Medical Co., Tokyo, Japan), from Nov. 2016 to Oct. 2017; we
used Ultra Stream HDMI (MAGEWELL Co., Nanjing, China) to capture display
signal from Fujinon systems and save as videos, during July 2019. All images
were annotated according to Yamada’s classification for colon polyp in white-
light colonoscopy with four types, though only used as one type, i.e., polyp,
in this study. Huashan Institutional Review Board (HIRB) approved this study.
All images, videos and text reports were deidentified.

Splits: We randomly split all 3056 labeled images into Image-train set with
2456 samples and Image-test set with 600 samples. We use all negative video
frames (60fps, around 1.5 million) as our Video-train set. We use all 5050
labeled positive frames as our Video-test set.

Here are two points to clarify. Firstly, although Video-test set with 5050
video frames seems larger than our image sets, it comes from only tens of
polyps. These frames are adjacent and similar. To avoid potential overfitting,
we only use them for testing. Secondly, the task of detection is different
from classification. Models need to distinguish polyps from the background and
point out their exact locations. Wrong position predictions will be counted as
false positives. Besides, clinical applications focus more on models’
sensitivity on positive samples. Hence, using only positive samples for
testing is acceptable.

####  3.1.2 Evaluation Metrics:

We use the average precision (AP) at IoU 0.5 to evaluate the polyp detection
performance, which is widely used in object detection tasks.

Table 1: Average Precisions (IoU = 0.5) of Polyp Detection. I for Image-train; V for Video-train. $\lambda$ follows the discrete distribution with $c=0.5,p=0.2$ in MixupDet. $\gamma=0.01$ in TCR.  Method | Training Set | AP on Image-test | AP on Video-test
---|---|---|---
Base [15, 11, 7] | I | 0.770 | 0.631
MixupDet | I + V | 0.778 | 0.714
TCR | I + V | 0.774 | 0.654
MixupDet + TCR | I + V | 0.791 | 0.717

###  3.2 Models and Training Settings

Models: We use Faster R-CNN [15] with FPN [11] as our basemodel, and ImageNet
[5] pre-trained ResNet-50 [7] as its backbone, i.e., $G$ in (5). $f$ in (5) is
the top FPN feature map, with 256 channels and a stride of 64 pixels with
respect to the input image.

Training Settings: We use momentum stochastic gradient descent (SGD) to train
all models, for 26 epochs unless otherwise stated. We use multi-step learning
rate with 1e-2 from epoch 0 to 15; 1e-3 from 16 to 21; 1e-4 from 22 to 25. We
apply random horizontal flip on images and corresponding labels with a
probability of 0.5. We freeze the batch normalization (BN) layers of the
backbone during training. Other hyperparameters are as follows: momentum 0.9,
weight-decay 1e-4, batch-size 8 (4 GPUs, 2 on each).

###  3.3 Evaluation of Mixup

Experiment results in Table 1 show the effectiveness of our mixup technique.
By mixing negative video frames into labeled images, the AP (IoU = 0.5) of
polyp detection on Video-test set increases significantly from 0.631 to 0.714.
Our mixup technique shrinks the semantic gap between images and videos.
Moreover, the AP on Image-test set also increases from 0.770 to 0.778,
implying the robustness of our algorithm. So the performance gain on Video-
test set is unlikely to be due to overfitting.

An ablation study in Table 2 further validates our algorithm, showing the
criticality of $\lambda$ ’s distribution. As a data augmentation technique,
mixup can control the model not to overfit. But a too strong mixup may cause
underfitting instead. Random variable $\lambda$ controls its strength. When
$\lambda$ follows $Beta(\alpha,\alpha+1)$, the lager $\alpha$ is, the stronger
mixup is. As in Table 2, when $\alpha$ rises, the detection performance
increases first and then decreases. When $\alpha$ equals 0.05, we get the best
AP of 0.707 in this distribution. We further test the discrete distribution of
$\lambda$. When $c$ equals 0.5 and $p$ equals 0.2, i.e., equally mixup image
and video inputs with a possibility of 0.2, we get an even higher AP of 0.714.
However, discrete distribution has two hyperparameters, $c$ and $p$, which is
hard to tune, especially when there are other regularization hyperparameters.

###  3.4 Evaluation of Temporal Coherence Regularization

Experiment results in Table 1 also indicate our temporal coherence
regularization (TCR) to be effective. With TCR applied, the AP (IoU = 0.5) of
polyp detection increases from 0.631 to 0.654 on Video-test set, and from
0.770 to 0.774 on Image-test set. Improvements on both sets validate that our
TCR can mine information from unlabeled videos. With TCR and our mixup
technique combined, the AP (IoU = 0.5) on Image-test set and Video-test set
further increases to 0.791 and 0.717 respectively.

Table 2: Ablation Study on MixupDet. Method | Distribution of $\lambda$ | Parameters | AP (IoU = 0.5) on Video-test
---|---|---|---
Base [15, 11, 7] | - | - | 0.631
MixupDet | $Beta(\alpha,\alpha+1)$ | $\alpha=0.02$ | 0.681
$\alpha=0.05$ | 0.707
$\alpha=0.1$ | 0.698
$\alpha=0.2$ | 0.675
$\alpha=0.5$ | 0.627
Discrete | $c=0.1,p=1.0$ | 0.650
$c=0.2,p=1.0$ | 0.665
$c=0.5,p=0.2$ | 0.714

##  4 Conclusion

In this study, we defined a clinically needed image-video-joint colonoscopy
polyp detection task, and collected a large-scale dataset from 917 positive
patients accordingly, over ten times larger than currently available public
datasets. We also proposed a novel Ivy-Net to alleviate the domain gap between
colonoscopy images from historical medical reports and real-time videos. We
further exploited the temporal information of unlabeled videos by using
temporal coherence regularization (TCR) on feature-level. Experiments on our
collected dataset demonstrate that our Ivy-Net with TCR achieves the state-of-
the-art result, improves the AP (IoU = 0.5) of polyp detection on colonoscopy
video significantly from 0.631 to 0.717. Our network is not limited in
colonoscopy polyp detection, and could be employed in any image to video joint
training task. We hope this study will offer the community an opportunity to
explore more powerful applications.

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