Shao et al., (2020) | Generative adversarial network, modified U-net | 6607 | - | 95.10 | 87.70 | 93.40
Javadi et al., (2020) | Multiple instance learning, conditional variational auto encoders | 339 | 66.00 | 77.00 | 55.00 | 68.00
Gilany et al., (2022) | Co-teaching paradigm, evidential deep learning | 472 | - | 67.38 | 88.20 | 87.76
Gilany et al., (2023) | Transformer, CNN, self-supervised learning (SSL) | 1398 | - | 88.00 | 51.00 | 80.00
Sun et al., (2023) | 3D CNN, prostate segmentation | 832 | - | 81.00 | 78.00 | 85.00
Wilson et al., (2023) | SSL, pretrain and fine-tune | 1028 | - | - | - | 90.99
Wu et al., 2024a  | 3D CNN, adaptive spatial fusion module, orthogonal regularization | 512 | 79.00 | - | - | 84.00
Wu et al., 2024b  | 3D CNN, hybrid attention module, prototype correction module | 512 | 81.00 | 86.00 | 73.00 | 86.00

##### Deep Learning Methods.

The DL methods for TRUS-based PCa diagnosis were primarily aimed at achieving
a more powerful feature representation that enhanced the classification
performance. Shi et al., (2016) employed a deep polynomial network (DPN) to
improve the representation performance of the initially extracted texture
features for small TRUS dataset, and further proposed a stacked DPN algorithm
to enhance the original DPN. Azizi et al., 2017a  employed deep belief
networks to automatically learn high-level features from CEUS data for PCa
detection and grading. Additionally, they utilized GMMs to model the
distribution of these features across different Gleason grades. Feng et al.,
(2018) proposed a 3D CNN to extract the spatial-temporal features uniformly
from the CEUS videos. The video frames were split into small image tensors,
and each tensor was treated as a sample. Shao et al., (2020) utilized a
generative adversarial network (GAN)-based three-player minimax game framework
to address data source heterogeneity and enhance classification performance
for PCa. A modified U-net was proposed to encode multi-scale PCa-related
information, thereby facilitating more effective data analysis and
classification. Javadi et al., (2020) employed multiple instance learning
(MIL) networks to learn from TRUS regions corresponding to biopsy core
pathology, which represented a statistical distribution of PCa. Independent
conditional variational auto encoders were trained to extract label-invariant
features from RF data, facilitating the generation of synthetic data that
enhanced MIL network training. Gilany et al., (2022) proposed a deep learning
model for the detection of PCa using micro-US. They employed a co-teaching
paradigm to handle label noise and incorporated evidential DL to estimate
predictive uncertainty. This combined approach addressed challenges such as
weak labels and out-of-distribution data. Gilany et al., (2023) proposed a
multi-scale and context-aware DL method for PCa detection from micro-US. Self-
supervised learning (SSL) was used to train a CNN for extracting features from
ROIs, while a Transformer model with attention mechanisms was employed for
feature aggregation. Sun et al., (2023) proposed a mask-guided hierarchical 3D
CNN for identifying clinically significant prostate cancer (csPCa) from TRUS
videos. Wilson et al., (2023) employed a SSL approach to leverage abundant
unlabeled micro-US data to train the PCa detection model. By pretraining
models with SSL and fine-tuning them on limited labeled data, the approach
outperformed traditional supervised learning methods. Wu et al., 2024a
proposed a framework for the classification of csPCa from multi-modality TRUS
videos. The framework utilized two encoders to extract features from B-mode
and SWE. An adaptive spatial fusion module was introduced to aggregate two
modalities’ features. An orthogonal regularized loss was further used to
mitigate feature redundancy. Wu et al., 2024b  further designed a hybrid
attention module to aggregate features from B-mode and SWE, then employed a
prototype correction module to refine these features for better classification
of csPCa from multi-modality TRUS videos.

###  3.4 Needle Detection

$\displaystyle\raisebox{-30.13885pt}{\rotatebox{90.0}{{Application
Scenarios}}}\begin{cases}\text{Biopsy: }\begin{aligned}
&\textit{\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{ding2004projection}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{krefting2007segmentation}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{cool2010temporal}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{ayvaci2011biopsy}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{tang2019real}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{mukhopadhyay2021deep}{\@@citephrase{(}}{\@@citephrase{)}}}}\\\
\end{aligned}\\\ \\\ \text{Brachytherapy: }\begin{aligned}
&\textit{\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{wei2004oblique}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{wei2005oblique}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{ding2006needle}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{yan2012automatic}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{buzurovic2012needle}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{qiu2013needle}{\@@citephrase{(}}{\@@citephrase{)}}},}\\\
&\textit{\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{qiu2014phase}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{waine20153d}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{waine2015three}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{hrinivich2017simultaneous}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{younes2018automatic}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{zhang2020weakly}{\@@citephrase{(}}{\@@citephrase{)}}}}\\\
&\textit{\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{wang2020deep}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{zhang2020multi1}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{zhang2020automatic}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{zhang2020multi3}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{zhang2020multi2}{\@@citephrase{(}}{\@@citephrase{)}}},
\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{zhang2021ultrasound}{\@@citephrase{(}}{\@@citephrase{)}}},}\\\
&\textit{\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{zhang2021multi}{\@@citephrase{(}}{\@@citephrase{)}}}}\end{aligned}\\\
\\\ \end{cases}$ Figure 10: Needle detection approaches categorized as
applications in biopsy and brachytherapy.

####  3.4.1 Biopsy and Brachytherapy

As shown in Fig. 10, most detection/segmentation approaches for interventional
needles from TRUS have been focused on the clinical scenarios of biopsy and
brachytherapy procedures. In the biopsy, needles are inserted to obtain
targeted tissue samples, which are analyzed to identify and diagnose PCa. In
the brachytherapy, needles are used to implant radioactive seeds directly into
cancerous tissues, maximizing treatment efficacy while minimizing damage to
healthy areas.

Table 6: Overview of methods on needle detection from TRUS.

References | Methods | Samples | Results
---|---|---|---
Shaft Error (mm) | Shaft Error (∘) | Tip Error (mm)
Wei et al., (2005) | Grey-level change detection, thresholding, linear regression | Phantom | - | 0.52$\pm$0.16 | 0.82$\pm$0.11
Cool et al., (2010) | Hough transform, temporal information | 108 | - | 2.30$\pm$2.00 | 2.10$\pm$4.00
Qiu et al., (2013) | 3D Hough transform | 35 | - | 0.80$\pm$0.21 | 0.90$\pm$0.32
Qiu et al., 2014c  | 3D phase grouping technique, 3D Hough transform | 50 | - | 0.95$\pm$0.15 | 1.15$\pm$0.15
Waine et al., 2015b  | Random sample consensus (RANSAC) | Phantom | 0.37$\pm$0.15 | - | -
Zhang et al., 2020a  | Bidirectional convolutional sparse coding, RANSAC | 10 | 0.11$\pm$0.09 | - | 0.32$\pm$0.35
Zhang et al., 2020e  | Large margin mask R-CNN | 23 | 0.09$\pm$0.04 | - | 0.32$\pm$0.30
Zhang et al., 2020c  | Unsupervised order-graph regularized sparse dictionary learning | 91 | 0.19$\pm$0.13 | - | 1.01$\pm$1.74

####  3.4.2 Needle Detection Methods

We categorize these methods into traditional methods and deep learning
methods. Some representative studies are given in Table 6.

##### Traditional Methods.

Wei et al., (2005) used grey-level change detection by comparing 3D TRUS
captured before and after needle insertion. The needle was segmented from the
difference map using a series of steps including thresholding, removal of
spurious signals, and linear regression. Waine et al., 2015b  employed random
sample consensus (RANSAC) to reconstruct the 3D needle shape during TRUS-
guided prostate brachytherapy. The Hough transform, traditionally employed in
image processing for the identification of geometrical shapes like lines and
circles, has been adapted for the segmentation of interventional needles in
TRUS. Cool et al., (2010) introduced a temporal-based algorithm that harnessed
the Hough transform to detect and track needle edges across multiple 2D TRUS
frames. Qiu et al., (2013) further employed the 3D Hough transform, leveraging
edge detection techniques in conjunction with assembly into coherent
structures. This method facilitated the estimation of needle deflection angles
with enhanced precision. Qiu et al., 2014c  grouped voxel gradient vectors
into line support regions (LSRs). Each LSR was processed using least-squares
fitting and a 3D randomized Hough transform to estimate the needle axis.

##### Deep Learning Methods.

In recent years, as DL has rapidly advanced, it has also gained increasing
prominence in the task of needle detection. Wang et al., (2020) developed a
modified U-net for segmenting pixels belonging to brachytherapy needles from
TRUS, supplemented by an additional VGG-16-based network that predicted the
locations of the needle tips. Zhang et al., 2020a  adopted a weakly supervised
framework that referenced corresponding CT images due to their higher signal-
to-noise ratio. The approach was centered around the bidirectional
convolutional sparse coding (BiCSC) model, which concurrently learned
dictionaries for both US and CT images to enhance needle visualization and
reduce noise. The whole process included image preprocessing, needle
reconstruction using the BiCSC model, and needle modeling through iterative
RANSAC algorithms. Zhang et al., 2020e  utilized a large margin mask R-CNN
model to localize needle shafts in TRUS, and further employed a needle-based
density-based spatial clustering of applications with noise (DBSCAN) algorithm
to refine shaft localization and detect needle tips. Zhang et al., 2020c
integrated sparse dictionary learning with order-graph regularization to
enhance spatial continuity in TRUS. The key idea lied in a label-free approach
that leveraged auxiliary images without needles to learn intrinsic features
for reconstructing target images.

##  4 Discussion and Future Research Directions

In this section, we discuss the current challenges and suggest possible
research directions for each of the main tasks in TRUS image processing.

###  4.1 Prostate Gland Segmentation

The evolution of TRUS segmentation techniques is pivotal for enhancing the
efficiency of PCa diagnosis and treatment. The intrinsic challenge in TRUS
segmentation arises from its imaging characteristics and the physiological
properties of the prostate, which collectively make the delineation of
prostate boundaries in TRUS ambiguously. Section 3.1 provides a systematic
review of the research progress in the field of TRUS segmentation, analyzing
it from the perspectives of imaging modalities, dimensions, and segmentation
methods. Diverse imaging modalities such as B-mode, elastography, and micro-US
offer various structural representations, aiding in the identification of the
prostate’s morphology and boundaries. Moreover, 2D, 2.5D, and 3D segmentation
methods each have their advantages, ranging from efficient segmentation of
single slices to comprehensive analysis of the entire volume, offering a
variety of technical options for accurate prostate segmentation. Traditional
segmentation methods and DL-based approaches each have their characteristics,
with DL showing great potential in improving segmentation accuracy.

Despite significant advancements in TRUS segmentation techniques, multiple
challenges remain. The ambiguity and noise in TRUS images pose a particular
difficulty for the automatic segmentation of prostate boundaries. Moreover,
while 3D segmentation methods offer more comprehensive structural information
of the prostate, their high computational costs and demands on computational
resources limit their application in clinical practice. The scarcity and
inconsistency of data quality also challenge the generalizability of models,
especially in surgical navigation and treatment planning, where high-precision
segmentation is required. The demand for real-time performance further
complicates the design of segmentation algorithms, particularly in surgical
environments where there are stringent requirements for the response speed of
the algorithms.

Future research should focus on enhancing the automation, accuracy, and real-
time performance of TRUS segmentation techniques. Automated and intelligent
algorithms will reduce user interaction, enhancing the efficiency and
repeatability of segmentation. Continuous optimization of DL models, including
improvements in network architecture and training strategies, will better
adapt to the specific requirements of prostate segmentation. Few-shot
learning, semi-supervised and weakly-supervised learning methods will address
the scarcity of high-quality annotated data, improving model performance with
limited data. Moreover, cross-modality and multi-task learning methods will
utilize the correlation between different imaging modalities and related
clinical tasks to enhance the overall effectiveness of segmentation. Finally,
improving the interpretability and generalizability of models, as well as
optimizing models to meet the demands of real-time processing, will be key to
advancing the development of prostate segmentation technology.

###  4.2 Prostate Image Registration

In the domain of PCa diagnosis and therapy, the registration of TRUS images
constitutes a critical technological component. By aligning preoperative
imaging (could be 3D MRI, CT, TRUS) with real-time 2D TRUS obtained during
surgery, it facilitates the precise targeting and treatment by clinicians.
Conventional approaches mainly include intensity-based methods and shape-based
methods, where the shape-based statistical model combined with biomechanical
modeling methods provide a robust set of solutions. Advancing with the
evolution of DL, registration methods driven by point clouds or annotated
labels have exhibited enhanced automation and precision by discerning
intricate mappings between sets of images.

Most of the articles we surveyed involve multi-modality registration,
predominantly focusing on 3D-3D registration. Consequently, shape-based
methods or label-driven networks are often employed in these tasks. These
methods are generally unaffected by different modalities and have demonstrated
promising results in preoperative applications. However, shape-based methods,
which register according to the prostate contours, tend to produce sparse
deformation fields, leading to sub-optimal deformation effects in internal
areas. Additionally, both shape-based and label-driven methods require
segmentation and annotation, which not only suffer from the segmentation
challenges discussed in Section 4.1 but also increase time complexity,
rendering them unsuitable for intraoperative scenarios. Intraoperative
registration involves the challenging leap from 3D to 2D dimensions, making it
difficult to ensure both accuracy and real-time performance simultaneously,
which is why an end-to-end method has yet to be developed. While image-based
methods can quickly generate dense deformation fields, their accuracy needs
improvement. Finding an effective similarity metric to measure different
modalities may be key to solving this problem.

Looking ahead to the future development of TRUS image registration, a suite of
innovative research directions is anticipated to propel the advancement of
this field. At the forefront, researchers are committed to crafting novel
algorithms that integrate detailed internal structural information of the
prostate, alongside the design of loss functions capable of surmounting the
disparities between various imaging modalities. Achieving real-time, end-to-
end registration from 2D TRUS to 3D MRI stands as a significant milestone,
promising to greatly enhance the precision and efficiency of surgical
navigation. Additionally, the integration of SWE into the registration process
to leverage the biomechanical properties of tissues for improved registration
accuracy is an important avenue for future exploration. Collectively, these
research directions aim to foster progress in TRUS image registration,
equipping the clinical realm with more accurate diagnostic and therapeutic
tools.

###  4.3 Prostate Cancer Classification and Detection

In the realm of PCa classification and detection, B-mode TRUS has emerged as a
predominant imaging modality, complemented by CEUS, Micro-US, and SWE to
enhance the accuracy of diagnosis. Traditional methods are primarily relied on
texture features, or combined with RF features for analysis through
classifiers like SVM. Moreover, radiomic analysis offers a comprehensive
feature assessment, extracting a vast array of quantitative features from
images. The application of DL has revolutionized PCa classification, with
various network structures and training strategies significantly improving
classification performance.

However, several challenges persist in this field. Data annotation remains a
difficult and time-consuming task due to the intricate nature of prostate
lesions in TRUS. Additionally, the heterogeneity of data sources and the
limited availability of high-quality labeled data complicate the development
of robust models. These issues underscore the need for innovative solutions to
enhance data quality and model performance.

Future research should explore the integration of multi-modality data and
advanced learning strategies to overcome these challenges. By combining
various imaging modalities and employing techniques like weakly-supervised and
semi-supervised learning, researchers can better utilize the vast amounts of
available but unlabeled data. Such approach can help mitigate the difficulties
in data annotation and improve the generalization capabilities of their
models. Furthermore, while most current studies focus on classification tasks,
there is a crucial need to develop methods for lesion-scale detection.
Advancing research in this direction will enable more precise localization and
characterization of cancerous regions, enhancing the overall efficacy of PCa
diagnostics in clinical practice.

###  4.4 Needle Detection

Recent advancements in interventional needle detection have leveraged both
traditional and DL methods. Traditional techniques, such as the Hough
transform and random sample consensus (RANSAC), have been used to detect and
track needle edges in TRUS images. These methods rely on identifying
geometrical shapes and managing noise and artifacts effectively. Meanwhile, DL
approaches have revolutionized needle detection by automatically learning
complex patterns from large datasets, improving the accuracy and efficiency of
needle detection.

Despite these advancements, several challenges remain in this field. The poor
imaging quality of TRUS often complicates the detection process. Furthermore,
there is often a discrepancy in annotating the gold standard, leading to
errors in needle segmentation. These issues highlight the need for more robust
techniques to enhance image quality and reduce annotation errors.

Future research should consider the inherent errors in the gold standard
annotations, weakly-supervised and semi-supervised learning methods should be
explored to improve model training. Further advancements could include
developing innovative algorithms that can better handle the inherent noise and
artifacts in TRUS images, paving the way for more precise and reliable
prostate needle segmentation methods. The integration of these techniques into
clinical workflows will further facilitate the delivery of personalized and
targeted therapies, contributing to the overall improvement in patient care
and outcomes.

##  5 Summary

This paper conducts a comprehensive review of the advancements in transrectal
ultrasound image processing. The paper meticulously reviews a plethora of
studies spanning two decades, delineating the paradigm shift from conventional
methods to deep learning approaches. For the segmentation, the paper
summarizes the traditional shape/contour-based, region-based, machine learning
methods, as well as the DL models. For 3D TRUS segmentation, the exigency for
high accuracy in preoperative planning, image registration, and volume
measurement is paramount, necessitating solutions to low image resolution and
large shape variations. Conversely, 2D TRUS segmentation confronts the
challenge of achieving real-time speed, especially in intraoperative
navigation scenarios. For the registration, the paper deliberates on the
efficacy of traditional intensity-based and shape-based methods, as well as
the burgeoning DL approaches, including image-based, label-driven, and point
set methods. It notes the predominance of multi-modality 3D-3D registration
tasks and the challenges associated with real-time property and accuracy in
intraoperative settings. For the PCa diagnosis, the paper highlights the
advancements made by traditional texture feature analysis and deep learning’s
capacity to discern complex patterns from large datasets. It acknowledges the
enduring challenges of data annotation and the heterogeneity of data sources,
advocating for innovative solutions to enhance data quality and model
performance. For the needle detection, traditional geometrical detection
methods and DL’s automatic pattern recognition have improved accuracy and
efficiency. This paper points out the necessity for more robust techniques to
address poor imaging quality and reduce annotation errors, suggesting the
exploration of weakly-supervised and semi-supervised learning methods as
future directions. In conclusion, this survey provides an exhaustive overview
of the current methodologies and challenges in prostate TRUS image processing
and suggests potential research directions to advance the field further.

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