# Sub-cortical structure segmentation database for young population

Jayanthi Sivaswamy  Center for Visual Information Technology, International
Institute of Information Technology (IIIT), Hyderabad, Telangana, India
corresponding author: Jayanthi Sivaswamy<EMAIL_ADDRESS> Alphin J
Thottupattu  Center for Visual Information Technology, International Institute
of Information Technology (IIIT), Hyderabad, Telangana, India  Mythri V
Center for Visual Information Technology, International Institute of
Information Technology (IIIT), Hyderabad, Telangana, India  Raghav Mehta
Center for Visual Information Technology, International Institute of
Information Technology (IIIT), Hyderabad, Telangana, India  Probabilistic
Vision Group, Centre for Intelligent Machines, Department of electrical and
Computer Engineering, McGill University, Montreal, QC, Canada  R Sheelakumari
Department of Imaging Sciences and Interventional Radiology, Sree Chitra
Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram,
Kerala, India  Chandrasekharan Kesavadas  Department of Imaging Sciences and
Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences
and Technology, Thiruvananthapuram, Kerala, India

###### Abstract

Segmentation of sub-cortical structures from MRI scans is of interest in many
neurological diagnosis. Since this is a laborious task machine learning and
specifically deep learning (DL) methods have become explored. The structural
complexity of the brain demands a large, high quality segmentation dataset to
develop good DL-based solutions for sub-cortical structure segmentation.
Towards this, we are releasing a set of 114, 1.5 Tesla, T1 MRI scans with
manual delineations for 14 sub-cortical structures. The scans in the dataset
were acquired from healthy young (21-30 years) subjects ( 58 male and 56
female) and all the structures are manually delineated by experienced
radiology experts. Segmentation experiments have been conducted with this
dataset and results demonstrate that accurate results can be obtained with
deep-learning methods. Our sub-cortical structure segmentation dataset, Indian
Brain Segmentation Dataset (IBSD) is made openly available at
https://doi.org/10.5281/zenodo.5656776.

## Background & Summary

The brain is the most complex organ in the human body and imaging is critical
to assess its structural and functional aspects. While a wide range of
modalities are available to image the brain, the most widely used ones are
Computed Tomography and Magnetic Resonance Imaging (MRI) as they aid
assessment of the structural aspects. Many automated image analysis techniques
have been developed to extract or segment the sub-cortical structures [1, 2,
3, 4, 5, 6]. Automating segmentation is attractive since manual delineation of
the structure boundaries (by experts) is very tedious. The sub-cortical region
of the brain is of key interest as it plays many vital roles such as emotion
control, hormone production, memory etc. Degradation of this region results in
many neurological disorders such as Alzheimer’s disease [7], Huntington’s
disease [8], Supra-nuclear Palsy [9], Schizophrenia [10], depression [11] etc.
Volumetric and shape analysis of sub-cortical structures of brain is commonly
used to study such disorders and hence accurate algorithms are needed for sub-
cortical structure segmentation. The success of Deep learning (DL) in computer
vision has also led to its application in the medical domain including for
developing algorithms for medical image segmentation. DL is largely a data-
driven paradigm which means data plays a critical role in training and a
developing a models for various tasks including segmentation. Hence, there is
a requirement for a dataset with adequate number of high quality images for a
variety of tasks such as segmenting structures of interest, aligning/
registering images, etc. However, the number of 3D datasets for brain image
segmentation available for public access is limited and the number of images
in these datasets is also not large enough to enable building of accurate
models for segmentation. Some of the widely used public datasets for brain
segmentation and the number of MR volumes and structures with markings/labels
are: i) MICCAI 2012 [12] with 35 MR volumes and 134 labeled structures ii)
IBSR [13] with 20 volumes and 43 labeled structures and iii) LPBA40 [14] with
40 volumes and 56 labeled structures iv) Hammers67n20 [15] with 20 volumes and
67 labeled structures and v) Hammers83n30 [15] with 30 volumes and 83 labeled
structures. Besides these, there are few public structure-specific datasets
with only (ex. hippocampus, [16]). private datasets such as introduced by
Babalola et al. provides 270 volumes and labels (via semi-automated
segmentation) for 18 structures. The neuroimages in these datasets are of
young to elderly individuals.
We present an Indian Brain Segmentation Dataset (IBSD), for sub-cortical brain
segmentation. This has 114 MR volumes generated under a fixed imaging
protocol. Each volume has 14 labeled sub-cortical structures. The number of MR
scans in the dataset is of approximately equal number of male and female
subjects belonging to a young age group (20-30 years). This data has been used
to create a template for the young Indian population [17]. Large number of
high quality MR images and segmentations is the main feature of our dataset.

## Methods

### MRI Image Collection

MR scans were collected from 114 young (21-30 years) healthy adult volunteers.
All the adults had completed their schooling and a majority of (> 90$\%$) them
also had an undergraduate level education. Healthy volunteers who had no past
history of head injury were selected for the study. Figure1 shows the age
distribution of all the volunteers. An experienced psychiatrist examined all
the volunteers and helped select only psychologically healthy subjects for the
study. A clinician and an experienced neurologist examined all the scans to
identify and exclude those with any structural abnormalities. Scans of 58 male
and 56 female volunteer were selected finally for inclusion in our study after
the scrutiny. Written consent was taken from all the volunteers for use of
their anonymized MRI scan for research purposes.

Figure 1: Age histogram of 114 volunteers

Scanning was done at three different sites which had different models of
scanners as follows. The sitewise distribution was as follows: 39 subjects
were scanned using Siemens 1.5T MRI scanner with T1 MPRAGE sequence, TE/TR/TI
= 2.9 / 2370/ 1000 ms and flip angle=7∘; 38 subjects were scanned using GE
1.5T MRI scanner with T1 BRAVO sequence TE/TR/TI = 4.2 /10.2/450 ms and flip
angle=15∘; and finally 37 subjects were scanned using Phillips 1.5T MRI
scanner with T1 3D TFE sequence with TE/TR/TI = 3.8/8.2/- and flip angle=7∘.
The imaging protocol was fixed to obtain scans with a voxel size of $1\times
1\times 1mm^{3}$ and a 3D matrix size of $256\times 256\times 192$. The MRI
volumes were acquired using 192 sagittal cuts.

### Preprocessing

All the MRI volumes were pre-processed using a standard pipeline consisting of
N4 Bias field correction [18] followed by denoising using Non-local Means
filtering [19]. Skull stripping was done with the Brain Extraction Tool[20]
and all the images were checked manually slice by slice (using ITK-SNAP) to
ensure good image quality after preprocessing.

### Sub-cortical segmentation labels

The seven sub-cortical structure pairs (Left and Right) chosen for markings
were the Thalamus, Putamen, Pallidum, Hippocampus, Amygdala, Caudate and
Accumbens area. All the manual markings were done by experts from Sree Chitra
Tirunal Institute for Medical Sciences and Technology, India. The seven
structures in each of the 2 hemispheres are illustrated with different colours
in 3 canonical views for a sample slice in Figure 2.

### Sub-cortical structure segmentation approach

The process of image marking/labeling had 4 sequential steps: i) automated
labeling, ii) label correction by a trained person, iii) label correction by a
radiologist and iv) label finalization by a senior neuroradiologist. In order
to perform the automated labeling, a set of 14 sub-cortical labels from
Talairach Daemon labels [21] were transferred to each brain MRI scan. These
were then manually edited using the ITK-SNAP [22] tool by a trained person to
correct for overshoots and filling the missing voxels. The first two steps
helped the radiologists to concentrate on fine tuning the markings and
producing good quality labels. A radiologist then corrected the labels by
overlaying them on the actual 3D MRI scans slice by slice. The corrected
labels were checked in the 3 canonical views (coronal, sagittal and axial
views) to verify the completeness of the 3D shape and labels. In the same way
the corrected labels were finalized by a senior neruoradiologist. Experts
delineated the structures using tissue intensity, relative position, and
structure shape information from their experience. 3D-mesh visualization of
each structure helped the experts to verify the delineated structure shape
with the expected shape in their mind. The slice-by-slice delineation and 3D
visualized cross-verification in each step helped the experts to work
efficiently.

Figure 2: Sub-cortical Structure labels of a sample subject image visualized
in 3 planes. The labels corresponding to each color mapped structure is given
on the right side.

## Data Records

All the 114 scans and their labels are made publicly available at
https://doi.org/10.5281/zenodo.5656776. The IBSD dataset is organised as
follows: T1 weighted preprocessed 3D MRI scans and corresponding label files
are stored in the main directory. Each label file has .ch.nii.gz extension
with same filename as the corresponding 3D MRI scan. All the image files are
stored in NIFTI format. The sub-cortical structure labels are numbered as
given in Figure 2.

## Technical Validation

The data quality of IBSD was checked by doing a comparison with other
datasets. This is first done via a visual comparison of the image and the
segmented structures. Next, IBSD data was used to train and test currently
popular non-DL algorithms as well as state of the art DL algorithms which
report results on public datasets. Specifically, segmentation was done with
two popular toolboxes namely the Freesurfer [23] and FIRST [24]; It was also
done with DL methods namely 3D U-net [25], Residual 3D U-net [26], Dense U-net
[27] V-net [28], M-net [5] and the state of the art $\psi$-net [6]. The
segmentation performance of Freesurfer [23], FIRST [24]; 3D U-net [25], and
$\psi$-net [6] was assessed on IBSD as well as two other public datasets
namely MICCAI 2012 [12] and IBSR [13] for comparison.

#### Evaluation Measures

Two commonly used metrics are used for the quantitative evaluation of the
segmentation methods. These are the Dice Similarity Coefficient (Dice) [29]
and the Hausdorff Distance (HD) [30]. Dice helps assess the degree of overlap
between the ground truth and computed segments while the HD helps capture any
tendency to over/under segment at a local level by a method. Since these two
metrics help assess the accuracy of a method at a global and local level they
are appropriate to evaluate accurate and spatially consistency of segmentation
results. Let A and B be the predicted segmentation and ground truth
respectively. The Dice coefficient is found by computing the overlap between
the computed segmentation result and the ground truth:

$Dice(A,B)=\frac{2\times|A\cap B|}{|A|+|B|},$ (1)

Where $|A\cap B|$ denotes the number of pixels in the overlapping region
between computed segmentation and ground truth while $|A|+|B|$ denotes the
number of pixels in A and B. The Dice score varies between 0 and 1, with 0
indicating no overlap or segmentation failure and 1 indicating complete
overlap with ground truth or perfect segmentation.

HD is a spatial distance based metric, unlike Dice which assesses the overlap.
HD therefore is based on computing the Euclidean distance between A and B as
well as B and A as follows.

$HD(A,B)=max(h(A,B),h(B,A)),$ (2) $h(A,B)=\max_{a\in A}\min_{b\in B}\parallel
a-b\parallel,$ (3)

$\parallel.\parallel$ is the Euclidean distance. Unlike Dice, $HD$ is not
bounded, however, lower values of $HD$ indicate better segmentation.

Dataset | Image Resolution | Age Range | # of subjects (Male:Female) |  | # of labels
---
(total: sub-cortical)
IBSR |  | 0.93x0.93x1.5
---
or 1x1x1.5 $mm^{3}$
Juvenile to 71 | 14:4 | 43:14
MICCAI2012 | 1x1x1.25 $mm^{3}$ | 18-90 | 22:13 | 134:14
LPBA40 | 2x2x2 $mm^{3}$ | 19.3-39.5 | 20:20 | 56:6
IBSD | 1x1x1$mm^{3}$ | 21-30 | 58:56 | 14:14
Table 1: Broad description of neuroimage datasets (of 1.5 T scans) for
segmentation.

### Visual assessment of IBSD data

Image resolution and quality have a major role in determining the quality of
final manual delineations. The specifications of various datasets such as
image quality parameters and subject information are listed for comparison in
Table 1. It can observed that IBSD has the highest number of scans (58+56 =
114) and the best image resolution (1mm isotropic voxel). A sample slice image
from 3 public datasets (LPBA40, IBSR, MICCAI 2012) and IBSD is shown in Fig.
3. The IBSD image is seen to be visually similar in the MICCAI 2012 dataset
due to the similarity in image resolution, though with better contrast.

Figure 3: Visual Comparison of quality of MR image in (first row) and
segmentation (second row) with central slices of the volume

### Validation of IBSD via automatic segmentation

Validation of IBSD was done using two types of automated segmentation
algorithms, namely those based on traditional machine learning and DL. If DL
methods demonstrate good segmentation performance then it can be inferred that
the size of the dataset and image quality are good as these DL methods are
data driven.
The performance figures of two commonly used conventional segmentation tools,
namely the Freesurfer [23] and FIRST [24], are presented in TABLE 2 while
those for DL-based methods are presented in Table 3. The Nucleus Accumbens
(Label 1-2) is significantly smaller than others which affects the automatic
segmentation performance. So, the performance excluding Accumbens’, i.e. only
for labels 3-14, is also reported in TABLE 2 and 3.
A six-fold cross validation was done to assess the DL methods by splitting the
114 volumes into six folds with 19 images in each; of these, four folds were
used for training, one fold for validation and one for testing. Six different
models were thus obtained, tested separately and the performance scores were
averaged and reported for each segmentation method.
When one compares the results in Table 3 with those for non-DL methods in
TABLE 2, it is apparent that the DL methods outperform the non-DL methods.
Among the DL methods, the Dense U-net shows the best performance on IBSD, with
a $14\%$ improvement in Dice and nearly 100% improvement in HD over the best
non-DL (FIRST) method. Exclusion/inclusion of Accumbens appears to impact the
Dice score but not the HD value as indicated by the figures in the last 2 rows
of the Table. A consistent performance is observed with DL methods (see Table
3) using U-net based architectures on the IBSD dataset as the Dice scores are
between 0.87 to 0.88 and HD value is between 2.9 and 5. This attests to the
integrity of the IBSD data.

Label | Structure | Freesurfer | FIRST
---|---|---|---
|  | Dice |  | HD
---
Dice |  | HD
---
1 | Right Nucleus Accumbens | 0.55 $\pm$ 0.08 | 7.14 $\pm$ 3.71 | 0.64 $\pm$ 0.09 | 6.27 $\pm$ 3.45
2 | Left Nucleus Accumbens | 0.51 $\pm$ 0.13 | 8.67 $\pm$ 3.37 | 0.57 $\pm$ 0.09 | 16.26 $\pm$ 10.42
3 | Right Amygdala | 0.66 $\pm$ 0.04 | 5.68 $\pm$ 4.7 | 0.71 $\pm$ 0.05 | 4.9 $\pm$ 4.82
4 | Left Amygdala | 0.67 $\pm$ 0.05 | 4.16 $\pm$ 0.8 | 0.71 $\pm$ 0.05 | 3.76 $\pm$ 1.06
5 | Right Caudate | 0.8 $\pm$ 0.03 | 8.02 $\pm$ 2.64 | 0.79 $\pm$ 0.04 | 5.31 $\pm$ 1.38
6 | Left Caudate | 0.81 $\pm$ 0.03 | 7.83 $\pm$ 2.48 | 0.79 $\pm$ 0.05 | 4.36 $\pm$ 1.43
7 | Right Hippocampus | 0.81 $\pm$ 0.03 | 4.18 $\pm$ 0.81 | 0.8 $\pm$ 0.03 | 3.96 $\pm$ 0.89
8 | Left Hippocampus | 0.77 $\pm$ 0.03 | 5.04 $\pm$ 0.96 | 0.76 $\pm$ 0.04 | 4.78 $\pm$ 1.28
9 | Right Pallidum | 0.77 $\pm$ 0.03 | 4.44 $\pm$ 1.2 | 0.79 $\pm$ 0.05 | 3.43 $\pm$ 0.67
10 | Left Pallidum | 0.69 $\pm$ 0.03 | 4.39 $\pm$ 1.19 | 0.78 $\pm$ 0.06 | 3.04 $\pm$ 0.61
11 | Right Putamen | 0.83 $\pm$ 0.03 | 5.31 $\pm$ 1.84 | 0.85 $\pm$ 0.03 | 4.95 $\pm$ 1.98
12 | Left Putamen | 0.82 $\pm$ 0.05 | 8.54 $\pm$ 2.84 | 0.85 $\pm$ 0.03 | 8.56 $\pm$ 2.88
13 | Right Thalamus | 0.84 $\pm$ 0.02 | 4.69 $\pm$ 0.53 | 0.86 $\pm$ 0.02 | 4.47 $\pm$ 0.7
14 | Left Thalamus | 0.83 $\pm$ 0.03 | 4.75 $\pm$ 0.89 | 0.86 $\pm$ 0.02 | 4.22 $\pm$ 0.81
| Average of label 3-14 | 0.78 $\pm$ 0.04 | 5.59 $\pm$ 1.74 | 0.8 $\pm$ 0.04 | 4.65 $\pm$ 1.54
| Full Average | 0.74 $\pm$ 0.05 | 5.92 $\pm$ 2 | 0.77 $\pm$ 0.05 | 5.59 $\pm$ 2.31
Table 2: Segmenttation performance of Freesurfer and FIRST on IBSD data. The
mean Mean$\pm$ Standard Deviation values are reported for Dice coefficient and
HD.

Label | Structure | 3D U-net |  | Residual 3D
---
U-net
Dense U-net | V-net | M-net | $\psi-net$
|  | Dice |  | HD
---
Dice |  | HD
---
Dice |  | HD
---
Dice |  | HD
---
Dice |  | HD
---
Dice |  | HD
---
1 |  | Right
---
Nucleus
Accumbens
| 0.751
---
$\pm$0.06
| 3.856
---
$\pm$0.147
| 0.767
---
$\pm$0.061
| 4.68
---
$\pm$1.827
| 0.778
---
$\pm$0.056
| 3.783
---
$\pm$0.412
| 0.74
---
$\pm$0.068
| 3.669
---
$\pm$0.628
| 0.788
---
$\pm$0.012
| 3.759
---
$\pm$0.472
| 0.782
---
$\pm$0.06
| 3.893
---
$\pm$0.373
2 |  | Left
---
Nucleus
Accumbens
| 0.708
---
$\pm$0.056
| 4.026
---
$\pm$0.377
| 0.72
---
$\pm$0.052
| 3.706
---
$\pm$0.244
| 0.728
---
$\pm$0.054
| 3.868
---
$\pm$0.15
| 0.7
---
$\pm$0.057
| 3.865
---
$\pm$0.475
| 0.735
---
$\pm$0.008
| 3.769
---
$\pm$0.215
| 0.729
---
$\pm$0.061
| 3.955
---
$\pm$0.308
3 |  | Right
---
Amygdala
| 0.849
---
$\pm$0.028
| 3.038
---
$\pm$0.858
| 0.86
---
$\pm$0.031
| 3.499
---
$\pm$1.224
| 0.864
---
$\pm$0.029
| 2.915
---
$\pm$0.842
| 0.839
---
$\pm$0.038
| 3.378
---
$\pm$1.112
| 0.872
---
$\pm$0.004
| 2.884
---
$\pm$0.733
| 0.867
---
$\pm$0.036
| 3.475
---
$\pm$1.472
4 |  | Left
---
Amygdala
| 0.828
---
$\pm$0.03
| 3.195
---
$\pm$0.299
| 0.841
---
$\pm$0.035
| 3.423
---
$\pm$0.85
| 0.846
---
$\pm$0.031
| 2.879
---
$\pm$0.358
| 0.805
---
$\pm$0.054
| 3.252
---
$\pm$0.54
| 0.854
---
$\pm$0.004
| 2.958
---
$\pm$0.63
| 0.849
---
$\pm$0.039
| 3.074
---
$\pm$0.379
5 |  | Right
---
Caudate
| 0.902
---
$\pm$0.015
| 3.11
---
$\pm$0.849
| 0.905
---
$\pm$0.017
| 3.512
---
$\pm$0.926
| 0.908
---
$\pm$0.014
| 2.911
---
$\pm$0.707
| 0.896
---
$\pm$0.031
| 2.718
---
$\pm$0.311
| 0.903
---
$\pm$0.003
| 2.864
---
$\pm$0.984
| 0.909
---
$\pm$0.015
| 7.952
---
$\pm$8.141
6 |  | Left
---
Caudate
| 0.902
---
$\pm$0.014
| 3.83
---
$\pm$1.854
| 0.906
---
$\pm$0.014
| 3.91
---
$\pm$0.487
| 0.908
---
$\pm$0.013
| 2.96
---
$\pm$0.158
| 0.898
---
$\pm$0.022
| 3.647
---
$\pm$1.319
| 0.903
---
$\pm$0.002
| 3.234
---
$\pm$1.073
| 0.909
---
$\pm$0.015
| 4.396
---
$\pm$1.216
7 |  | Right
---
Hippocampus
| 0.885
---
$\pm$0.013
| 3.884
---
$\pm$1.236
| 0.891
---
$\pm$0.012
| 3.903
---
$\pm$2.283
| 0.894
---
$\pm$0.011
| 2.688
---
$\pm$0.265
| 0.875
---
$\pm$0.02
| 3.584
---
$\pm$0.717
| 0.889
---
$\pm$0.004
| 3.361
---
$\pm$1.103
| 0.898
---
$\pm$0.012
| 7.533
---
$\pm$3.66
8 |  | Left
---
Hippocampus
| 0.871
---
$\pm$0.017
| 3.715
---
$\pm$1.516
| 0.876
---
$\pm$0.017
| 3.382
---
$\pm$0.548
| 0.88
---
$\pm$0.016
| 3.086
---
$\pm$0.479
| 0.858
---
$\pm$0.029
| 3.28
---
$\pm$0.305
| 0.874
---
$\pm$0.003
| 3.709
---
$\pm$1.314
| 0.883
---
$\pm$0.019
| 6.934
---
$\pm$3.323
9 |  | Right
---
Pallidum
| 0.88
---
$\pm$0.017
| 2.021
---
$\pm$0.101
| 0.887
---
$\pm$0.017
| 2.983
---
$\pm$1.996
| 0.893
---
$\pm$0.015
| 1.811
---
$\pm$0.075
| 0.868
---
$\pm$0.021
| 2.275
---
$\pm$0.134
| 0.893
---
$\pm$0.003
| 2.106
---
$\pm$0.769
| 0.904
---
$\pm$0.014
| 2.734
---
$\pm$2.176
10 |  | Left
---
Pallidum
| 0.871
---
$\pm$0.022
| 2.121
---
$\pm$0.076
| 0.882
---
$\pm$0.021
| 2.029
---
$\pm$0.113
| 0.883
---
$\pm$0.021
| 2.01
---
$\pm$0.133
| 0.86
---
$\pm$0.027
| 2.345
---
$\pm$0.189
| 0.886
---
$\pm$0.002
| 2.351
---
$\pm$0.707
| 0.895
---
$\pm$0.02
| 2.176
---
$\pm$0.129
11 |  | Right
---
Putamen
| 0.907
---
$\pm$0.013
| 3.213
---
$\pm$0.134
| 0.911
---
$\pm$0.013
| 3.783
---
$\pm$1.108
| 0.915
---
$\pm$0.014
| 3.206
---
$\pm$0.161
| 0.904
---
$\pm$0.02
| 3.424
---
$\pm$0.509
| 0.908
---
$\pm$0.001
| 3.027
---
$\pm$0.564
| 0.92
---
$\pm$0.012
| 4.511
---
$\pm$2.214
12 |  | Left
---
Putamen
| 0.902
---
$\pm$0.014
| 3.747
---
$\pm$0.441
| 0.906
---
$\pm$0.014
| 3.671
---
$\pm$0.719
| 0.909
---
$\pm$0.013
| 4.126
---
$\pm$1.419
| 0.899
---
$\pm$0.02
| 4.071
---
$\pm$1.358
| 0.903
---
$\pm$0.001
| 3.565
---
$\pm$0.496
| 0.913
---
$\pm$0.015
| 6.973
---
$\pm$3.541
13 |  | Right
---
Thalamus
| 0.928
---
$\pm$0.01
| 2.704
---
$\pm$0.6
| 0.931
---
$\pm$0.009
| 2.794
---
$\pm$0.625
| 0.934
---
$\pm$0.008
| 2.377
---
$\pm$0.051
| 0.923
---
$\pm$0.012
| 2.648
---
$\pm$0.215
| 0.924
---
$\pm$0.002
| 2.787
---
$\pm$0.692
| 0.938
---
$\pm$0.008
| 4.795
---
$\pm$2.619
14 |  | Left
---
Thalamus
| 0.928
---
$\pm$0.01
| 3.858
---
$\pm$1.212
| 0.931
---
$\pm$0.01
| 4.527
---
$\pm$1.486
| 0.934
---
$\pm$0.009
| 2.544
---
$\pm$0.12
| 0.922
---
$\pm$0.013
| 2.896
---
$\pm$0.43
| 0.923
---
$\pm$0.002
| 3.219
---
$\pm$0.933
| 0.937
---
$\pm$0.01
| 6.928
---
$\pm$2.766
|  | Average
---
of
label 3-14
| 0.888
---
$\pm$0.017
| 3.203
---
$\pm$0.368
| 0.894
---
$\pm$0.017
| 3.451
---
$\pm$0.406
| 0.897
---
$\pm$0.016
| 2.793
---
$\pm$0.057
| 0.879
---
$\pm$0.026
| 3.126
---
$\pm$0.489
| 0.894
---
$\pm$0.003
| 3.005
---
$\pm$0.833
| 0.902
---
$\pm$0.027
| 5.123
---
$\pm$1.463
|  | Full
---
Average
| 0.865
---
$\pm$0.023
| 3.308
---
$\pm$0.332
| 0.872
---
$\pm$0.023
| 3.557
---
$\pm$0.442
| 0.877
---
$\pm$0.022
| 2.94
---
$\pm$0.041
| 0.856
---
$\pm$0.031
| 3.218
---
$\pm$0.467
| 0.867
---
$\pm$0.002
| 3.005
---
$\pm$0.734
| 0.881
---
$\pm$0.065
| 4.952
---
$\pm$1.23

Table 3: Performance of 3D Unet, Residual 3D Unet, Dense U-net and V-net with
IBSD data in terms of dice coefficient and HD with respect to Ground truth.
The values are in Mean$\pm$ Standard Deviation format

### Segmentation performance on other Datasets

It is well known that the performance of segmentation algorithms depend on
many aspects of the dataset, such as number of images in the set, image
quality and structural details. As a final experiment three models were
trained and tested on two public datasets (IBSR and MICCAI 2012) and compared
with that on IBSD. The obtained results are presented in Table 4, with the
best results for each method indicated in bold font. From this Table, it can
be observed that training on IBSD yields best Dice consistently for all 3 DL
methods (last 3 rows) and lowest HD value are obtained for 2 of 3 DL methods.
Since IBSD has the maximum number of images, this result reaffirms the data
dependent nature of DL and emphasises the need for a larger dataset for
segmentation.

| IBSR | MICCAI | IBSD |
---|---|---|---|---
| Dice |  | Hausdorff
---
Distance
Dice |  | Hausdorff
---
Distance
Dice | HD
3D-Unet | 0.842 $\pm$ 0.052 | 3.52 $\pm$ 0.86 | 0.840 $\pm$ 0.047 | 4.01 $\pm$ 1.87 | 0.865 $\pm$0.023 | 3.308 $\pm$0.332
Dense U-net | 0.846 $\pm$ 0.092 | 3.41 $\pm$1.27 | 0.864 $\pm$ 0.055 | 3.24 $\pm$1.64 | 0.877 $\pm$0.022 | 2.94 $\pm$0.041
$\psi$-net | 0.855 $\pm$ 0.074 | 3.02 $\pm$ 0.55 | 0.875 $\pm$ 0.056 | 2.76 $\pm$ 0.72 | 0.881 $\pm$0.065 | 4.952 $\pm$1.23
Table 4: Free-surfer,FIRST, 3D U-net, Dense U-net and $\psi$-net performance
compared with IBSR, MICCAI and IBSD data with average dice coefficient and HD
for 14 sub-cortical structures.The values are in Mean$\pm$ Standard Deviation
format

## Code availability

All the prepossessing steps and experiments were done with publicly available
software. We have performed Bias Field correction and denoising with ANTS
software commands N4BiasFiledCorrection and DenoiseImage repsectively from
http://stnava.github.io/ANTs/. We have used BET command from FSL available in
https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET/ to perform skull stripping.We have
used ITK-SNAP 3.8.0 to perform manual segmentation corrections which is
available in http://www.itksnap.org/pmwiki/pmwiki.php. The segmentation
methods we have used in the experiment section are also publicly available. We
have used Freesurfer from https://surfer.nmr.mgh.harvard.edu/, 3d unet from
https://github.com/wolny/pytorch-3dunet, residual 3d unet from
https://github.com/wolny/pytorch-3dunet, v-net from
https://github.com/faustomilletari/VNet, Mnet from
https://github.com/Prathyusha-Akundi/M-Net and psi-net from
https://github.com/lihaoliu-cambridge/psi-net.

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## Acknowledgements

We acknowledge IHub-Data, IIIT Hyderabad, India for financial assistance for
publication.

## Author contributions statement

J.S. conceptualized the work, A.J. and M.V. equally contributed the data
analysis, R.M. collected the MRI scans while he was in IIIT-Hydearabad,
S.K.(radiology expert ) and C.K.(Senior radiologist) done the manual
segmentations. All authors reviewed the manuscript.

## Competing interests

The authors declare no competing interests.