3D MRI Image Segmentation using 3D UNet Architectures: Technical
Vijaya Kamble
and Rohin Daruwala
Research Scholar Department of Electronics Engineering, Veermata Jijabai Technological Institute (VJTI),
Mumbai, Maharashtra, India
Department of Electronics Engineering, Veermata Jijabai Technological Institute (VJTI), Mumbai, Maharashtra, India
Keywords: 3D UNet, MRI
Images, Segmentation, Brain Tumors
Abstract: From last few decades machine learning & deep convolutional neural networks (CNNs) used extensively and
have shown remarkable performance in almost all fields including medical diagnostics. It is used in medical
domain for automatic tissue, lesion detection, segmentation, anatomical or structure segmentation classifica-
tion & survival predictions. In this paper we presented an extensive technical literature review on 3D CNN
U-Net architectures applied for 3D brain magnetic resonance imaging (MRI) analysis. We mainly focused on
the architectures, its modifications, pre-processing techniques, types datasets, data preparation, methodology,
GPU, tumor disease types and per architectures evaluation measures in this works. Our primary goal for this
extensive technical review is to report how different 3D U-Net architectures or CNN architectures have been
used to differentiate between state-of-the-art strategies, compare their results obtained using public/clinical
datasets and examine their effectiveness. This paper is intended to present detailed reference for further re-
search activity or plan of strategy to use 3D U-Nets for brain MRI automated tumor diseases detection, seg-
mentation & survival prediction analysis. Finally, we are presenting a novel perspective to assist research
directions on the future of CNNs & 3D U-Net architectures to explore in subsequent years to help doctors &
Over last few decades the use of machine learning and
deep learning techniques revolutionized medical
imaging field for tumor or disease segmentation,
detection & survival predication. It is helping
physicians to diagnose brain cancers quickly to boost
prognosis. A patientโ€™s MRI is the three-dimensional
brain anatomy (Oday Ali Hassen, et al., 2021). MRI
images of different modalities such as T1-weighted,
T2-weighted, T1c, and Flair as T1c has precise data
such as tumor form, location, and scale. Different
MRI modalities are used in brain tumor extraction
and segmentation. Among all types of brain tumors
Gliomas are most common tumors in brain cancer
with high mortality rate. These brain tumors
originating from the glial cells in the central nervous
system. Gliomas are 70% of all brain tumors. The
survival duration of patients with high grade gliomas
(HGG) lead less than 2 years if prognosis is poor.
Compared with HGG, prognosis of low grade
gliomas (LGG) are more effective (Chandan
Yogananda, et al., 2020).
Different architectures of CNNs used in medical
imaging and other applications from year 1990s.
Medical Image data is sensitive patients data and not
available easily. Earlier limitations on on
performance of CNN networks years as less labeled
medical data available. But now large annotated
medical public & clinical data sets available online &
on demand and more powerful graphics processing
units (GPUs) available for data processing so this is
enabling researchers to continue working in the area
to help doctors (Chandan Yogananda, et al., 2020).
Automated or semi automated segmentation
methods saving physicians time and provide an
accurate reproducible solutions for 3D brain tumor
analysis and patient monitoring. Convolutional neural
Kamble, V. and Daruwala, R.
3D MRI Image Segmentation using 3D UNet Architectures: Technical Review.
DOI: 10.5220/0010851300003123
In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING, pages 141-146
ISBN: 978-989-758-552-4; ISSN: 2184-4305
๎€ 2022 by SCITEPRESS โ€“ Science and Technology Publications, Lda. All rights reserved
networks (CNN) able to learn from examples so they
demonstrate state-of-the-art segmentation accuracy
both in 2D natural images (Andriy Myronenko, 2019)
and in 3D medical image modalities. Its difficult to
differentiate brain tumors from normal tissues
because tumor boundaries are ambiguous and there is
a high degree of variability in the shape, location,
intensity in homogeneity, or different intensity ranges
between the same sequences and acquisition scanners
and extent of the patient (Li Sun, et al., 2019). This
can influence the segmentation accuracy and correct
detection of tumor. Different hospitals shows
different gray-scale values for the same tumorous
cells may when they are scanned differently.
Although advance automatic algorithms used for
brain tumor segmentation, the problem is still remains
a challenging task.
To address issues in this research area we have
done extensive comparative review of most cited
research papers based on 3D U-Net architectures &
3D medical imaging modalities, with different
processing techniques use of powerful GPUs
different software's with various high grade tumors
classification segmentation & survival prediction of
Summary of this extensive most cited research is
mentioned in table no 1 with reference to paper, few
prominent U-Net model parameter & methodology
discussed in short with figure. Different imaging
modalities, preprocessing techniques datasets,
evaluation parameters advantages & limitations also
mentioned. Most of the reviewed content got dice
scores above 0.75 to 0.89 range for whole tumor core
tumors & enhancing tumors. Some of the papers got
excellent accuracy, sensitivity & specificity.
CNN architectures used in medical imaging for
segmentation detection & predictions of disease
diagnosis prognosis. CNN architectures can be
grouped around five sub types:
I) Based on interconnected operating modules,
II) Selection of types of input MRI modalities,
III) Selection of input patch dimension,
IV) Number of Predictions at a time
V) Based on implicit and explicit contextual
In this summary of the literature review methods dis-
tinguish with the different CNN architectures mostly
on types of U-Nets, pre processing, post-processing
and target of the segmentation & tumor types.
2.1 UNet Architectures Literature
In medical imaging for brain tumor disease diagnosis
prognosis for image semantic segmentation and
classifications mostly U-Net ResNet architectures are
The U-Net is one of the most popular convolu-
tional neural network end-to-end architectures in the
field of semantic segmentation.a that is designed for
fast and precise segmentation of images. In several
challenges U-Net has performed extremely well.
Figure 1: UNet Architecture (Xue Feng et al.).
U-Net Architecure split the network into two parts:
Encoder: The encoder path is the backbone. The
encoder captures features at different scales of the
images by using a traditional stack of convolutional
and max pooling layers. A block in the encoder
consists of the repeated use of two convolutional
layers (k=3, s=1), each followed by a non-linearity
layer, and a max-pooling layer (k=2, s=2). For every
convolution block and its associated max pooling
operation, the number of feature maps is doubled to
ensure that the network can learn the complex
structures effectively.
Decoder: The decoder path is a symmetric expanding
counterpart that uses transposed convolutions. This
type of convolutional layer is an up-sampling method
with trainable parameters and performs the reverse of
(down)pooling layers such as the max pool. Similar to
the encoder, each convolution block is followed by
such an up-convolutional layer. The number of feature
maps is halved in every block. Because recreating a
segmentation mask from a small feature map is a
rather difficult task for the network, the output after
every up-convolutional layer is appended by the
feature maps of the corresponding encoder block. The
feature maps of the encoder layer are cropped if the
BIOIMAGING 2022 - 9th International Conference on Bioimaging
dimensions exceed the one of the corresponding
decoder layers.
In the end, the output passes another convolution
layer (k=1, s=1) with the number of feature maps
being equal to the number of defined labels. The
result is a u-shaped convolutional network that offers
an elegant solution for good localization and use of
context. Letโ€™s take a look at the code. max pooling
operations (in each dimension) are the most
In these section the from most cited literature
review best architecture discussed. Researchers
proposed common U-nets, cascaded U-Nets,
modified type of Unet architectures for brain tumor
detection & survival predictions.
Xue Feng et al. explained generic 3D U-Net
structure with different hyper-parameters, deployment
of each model is for full volume prediction and final
ensemble modeling. Model fitting done for the survival
task feature extraction (Xue Feng, et al., 2019).
Figure 2: Cascaded Unet Architecture (Yan Hu, et al.).
Yan Hu et al proposed algorithm for intra-tumor
structure segmentation using three cascaded U-Net
models as shown in figure 2. They are concatenated
and further processed by two convolutional layers to
detect tumor region. The feature maps generated by
three cascaded U-Net models using T1, T1c, T2 and
FLAIR modalities.Patches are cropped within tumor
region detected for classification model (Yan Hu , et
al., 2018).
2.2 Pre-processing
In computer vision or image processing domain pre-
processing of image is preliminary but important task.
Brain MRI volumes acquired from scanners, these
volumes are with nonbrain tissues, parts of the head
or skull, eyes, fat, spinal cord. From acquired MRI
volumes extracting the brain tissue from non-brain
image is the pre processing primary task. This is
known as skull strippings. This is an essential step for
subsequent segmentation task. To achieve a good
performance in training supervised models such as
CNNs, or Unets the input training data hugely
influences the performance of the model, so having
preprocessed and well-annotated data is a crucial step
in MRI image processing. This step is very important
as it has direct impact on the performance of auto-
mated segmentation methods. Inclusion of skull or
eyes as brain tissue in MRI analysis may lead to
unexpected results missed classification or tumor
detection. In this review context every researcher
used some preprocessing methods & post processing
method for correct segmentation, predictions results.
In MRI image preprocessing there are few
common but fixed steps or algorithms stated as below:
i) Intensity Normalization-as there are different
image modalities,
ii) Bias field Corrections,
iii) Skull stripping,
iv) Image registration.
After Image preprocessing step there is data
preparation phase in CNN algorithms in that data
augmentation, 2D, 3D patch extraction before
segmentation & classification task.
2.3 Input Modalities
There are various types of MRI images based on their
scanning techniques, acquisition modes, intensities.
Basically in MRI four modalities are popular among
research community T1,T2 T2c Flair. In the literature
strategies of selection of modality for processing can
also be grouped according to the number of
modalities that are processed at the same time. The
major categories are two: single- and multi-modality.
Final Deep CNN models performance majorly
depend on the types of dataset, modalities,
types of
tumors regions, sub-regions & model parameters. In
this extensive research survey of 3D UNet & MRI
imaging following evaluation metrics were used for
segmentation & classification of tumors:
i) Global accuracy,
ii) Dice coefficient,
iii) Recall,
iv) Precision and
v) Housedraf distance measure.
3D MRI Image Segmentation using 3D UNet Architectures: Technical Review
Following are the 6 Equations for evaluation
parameters with the well known terms False Positives
(FP), False Negatives (FN), True Negative(TN):
๐บ๐‘™๐‘œ๐‘๐‘Ž๐‘™๐ด๐‘๐‘๐‘ข๐‘Ÿ๐‘Ž๐‘๐‘ฆ ๎ตŒ
๐‘‡๐‘ƒ ๎ต… ๐‘‡๐‘
๐‘ƒ๐‘Ÿ๐‘’๐‘๐‘–๐‘ ๐‘–๐‘œ๐‘› ๎ตŒ
๐‘…๐‘’๐‘๐‘Ž๐‘™๐‘™ ๎ตŒ
๐น1 ๎ตŒ 2
๐‘ƒ๐‘Ÿ๐‘’๐‘๐‘–๐‘ ๐‘–๐‘œ๐‘› โˆ— ๐‘…๐‘’๐‘๐‘Ž๐‘™๐‘™
๐‘ƒ๐‘Ÿ๐‘’๐‘๐‘–๐‘ ๐‘–๐‘œ๐‘› ๎ต… ๐‘…๐‘’๐‘๐‘Ž๐‘™๐‘™
๐ท๐‘–๐‘๐‘’ ๎ตŒ
๐‘†๐‘๐‘’๐‘๐‘–๐‘“๐‘–๐‘๐‘–๐‘ก๐‘ฆ ๎ตŒ
The main evaluation measures for the challenges
mentioned pre-viously are DSC, specificity,
sensitivity, positive predictive value (pre-cision),
average surface distance (ASD), average volumetric
difference (AVD) and modified Hausdorff distance
Table 1 summarizes the types of architectures,
databases, numbers of samples, MRI modalities
considered, tumor diseases types, GPU types,
Software's used, evaluation measurements applied
and corresponding results reported of the extensive
technical surveyed work (Chandan Yogananda, et al.,
2020 - http://www.tomography.org/).
Now a days most of researchers release their winning
competitions or challenges source codes to the public
it helps for research in the medical & other fields.
There are few free deep learning libraries for MRI
segmentation as listed below:
i) Tensorflow,
ii) Theano,
iii) Caffe,
iv) Keras
v) PyTorch.
There are few CNN open-source frameworks
namely NiftyNet 17 and DLTK.
Researchers work on clinical & publically
available datasets depending on their application.
Automatic brain tumor segmentation for cancer
diagnosis & prediction is challenging task. Most recent
advancements in medical diagnostic research using
Deep Convolution Neural network 3D Unet
architectures discussed in this technical most cited
literature review paper. This 3D Unet architectures &
modified frameworks indicate significant potential to
segment classify &predict the brain tumors lesions
from the 3D MRI images. Even though MRI images
are of different modalities intensities and categories
still complex features from these MRI images can be
automatically extracted from 3D Unet architectures it
also segment tumor with subregions. There is always
chance of improvements and modifications in CNN
architectures, Unet architectures to improve the
efficiency of segmentation, detection & predictions of
cancerous brain tumors.
With this deep technical review we observed and
analyzed that most of the proposed methods are based
on specific 3D MRI modalities for high grade tumor
segmentation so they have computational
complexities as well as memory constraints & in need
of specific GPU speed for software's. In most of
papers deep learning software libraries are used to
implement layers of deep CNNs. They are arranged
either parallel or distributed or cascaded frameworks,
which help researchers to train their models in multi-
core architectures or GPUs. Mostly Nvidia GPUs &
Intel GPUs used for training and implementation of 3
D Unet CNN models. It is observed from evaluation
measures that the training and validation for brain
image analysis is significantly affected by the data
imbalance problem .Lesions are smaller than the
entire volume so it affects generalization & robust
model. We observed that the full capacity of 3D Unet
CNN architectures has not yet been fully leveraged in
brain MRI analysis. More sophisticated dedicated
softwares are available for Medical imaging or Brain
MRI analysis. But there is always a challenge for
domain adaptation techniques, more research in this
sense is needed for permanent solutions for high
grade and low grade tumors for correct diagnosis
without experts interventions.
BIOIMAGING 2022 - 9th International Conference on Bioimaging
Table 1: Comparison with different U-Net models.
SrNo Article Dataset Number of scans Model GPU Softwares Segmentation
Evaluation Measure
1 Yue Zhao
et al.
BraTS 2018 75 low grade and 210 high
grade gliomas
3D recurrent
multi-fiber network
V100 32 GB GPU.
Pytorch MS lesion
Whole brain,
tissue and sub-
Dice scores of WT 89.62%,
TC 83.65% and ET 78.72%
2 Chandan
et al
Oslo data set 52
(age > 18 years)
scanned from
2003 to 2012
(200 cases 150 HGG and 50
LGG), validation (65 48 HGG
and 17 LGG) and 10% (20 12
HGG and 8 LGG)
Combination of WT-net,
TC net, ET net
Tesla P100, P40 or
ackage, and
IDEs with
MS lesion
Whole brain,
tissue and sub-
Dice-scores for WT 0.90, TC
0.84, and ET 0.80
3 Oday Ali
Hassen et al
BRATS 2019
2017. At
BRATS 2019
3D-MRI of 336 heterogeneous
gliomas patients, 259 HGG and
76 Low-Grade Gliomas LGG
Population-based Artificial
Bee Colony Clustering (P-
ABCC) methodology, K-
Intel (R), Core
(TM) i3 CPU, 8.00
Brain Tumour Entire Tumor (WT), Tumor
Center (TC), Improved (ET) by
0.03%, 0.03%, and 0.01%
respectively. At BRATS 2017,
an increase in precision for WT
was reached by 5.27%.
Jing Huang
and Minhua
Zheng et al
BRATS 2017
285 patients, 210 HGG images,
75 LGG images
Brain Tumour
Dice scores Similarity
WT 0.9089, TC 0.7165, and
ET 0.8398
5 Parvez
Ahmad et
228 training images 57 testing
images out of data set is 285.
Residual 3D U-net, Dense
inception-like architecture
with multiple dilated
convolutional layers
Keras Brain Tumour Dice Similarity
WT 87.16, ET 84.81, 80.20,
Whole 86.42, Sensitivity
Core, Enhancing 82.15,80.01
6 Hassan A.
Khalil et al
BRATS 2017 Clustering technique
integrates k-means and the
dragonfly algorithm
Intel, Core i3 CPU
with 8.00 GB of
Brain Tumour Accuracy 98.20, Recall 95.13,
Precision 93.21
7 Xue Feng
et al
CBICAโ€™s Image
163 training subjects, 285
training subjects, 66 subjects
were provided as validation
An ensemble of 3D U-Nets
with different hyper-
parameters for brain tumor
Nvidia Titan Xp
GPU with 12 Gb
was used
with Adam
Brain Tumour Accuracy was 0.321, MSE
was 99115.86, median SE
was 77757.86, std SE was
104291.596 and Spearman
Coefficient was 0.264
et al
BraTS 2018
285 Training cases validation
(66 cases) and the testing sets
(191 cases)
Encoder-decoder based
CNN architecture
asymmetrically larger
encoder to smaller decoder
V100 32 GB GPU
Tensorflow Brain Tumour
Dice Similarity ET 0.7664,
WT 0.8839 and TC 0.8154
Wei Chen,
Liu et al.
BraTS 2018
285 subjects, of which 210 are
GBM/HGG and 75 are LGG
Separable 3D U-Net
GeForce GTX
1080Ti GPU
Brain Tumor
Dice scores of ET 0. 68946,
WT 0. 83893 and TC 0.
10 Xiaojun Hu
et al.
BRATS 2015,
ISLES 2017
3D Brain SegNet 4 Titan Xp
GPUs,8G memory
for each GPU
Pytorch Brain Tumor Dice Score 0.30ยฑ 0.22,
0.35ยฑ0.27, 0.43ยฑ0.27
11 Li Sun et
BraTS 2018 210 HGG and 75 LGG Three different 3D CNN
architectures (CA-CNN,
DFKZ Net, 3D U-Net,
Wnet, Tnet, Enet)
Brain Tumor,
61.0% accuracy
12 Dmitry
Lachinov et
BraTS 2018 285 MRIs for training (210
high grade and 75 low grade
glioma images), 67 validation
and 192 testing MRIs.
Multiple Encoders Unet,
Cascaded UNet
GTX 1080TI MXNet
Brain Tumor,
Dice score of ET 0.720, WT
0.878, TC 0.785
Ping Liu et
BraTS 2017
285 samples with manually
annotated and confirmed
ground truth labels
Deep supervised 3D
Squeeze-and-Excitation V-
Net (DSSE-V-Net)
4 NVIDIA Titan
1080 TI 11GB
Brain Tumor,
Dices of WT and TC of DS-U-
Net increased to 0.8953 and
0.7828 from 0.8799 and 07693
of 3D U-Net, respectively
et al
BRATS 2017
285 scans (210 high grade
gliomas and 75 low grade
CNN-based model,short-
range 3D context and the
long-range 2D context
Brain Tumor,
Dice scores of WT 0.918, TC
0.883 ET 0.854
15 Suting
Peng et al
BraTS 2015 220 HGG and 54 LGG Multi-Scale 3D U-Nets
GTX 1080Ti GPU
Brain Tumor,
Dice similarity
WT 0.85, ET 0.72, WC 0.61
16 Mina
Ghaffari et
BraTS 2018 230 cases for training, and the
remaining 55 cases were
reserved for testing.
Modified version of the
well-known U-Net
4 x
Pascal P100
Brain Tumor Dice similarity WT,0.87, ET
0.79 WC0.66
17 Parvez
Ahmad et
BraTS 2018 80% of subjects for training and
20% for validation
3D Dense Dilated
Hierarchical Architecture
Brain Tumor, Dice similarity
WT 0.8480, TC 0.8574 CT
Lu et al
259 high-grade gliomas (HGG)
and 76 low-grade gliomas
Multipath feature extraction
NVIDIA 1080ti
GPU with 11G
pytorch Brain Tumour
Dice similarity
WT 0.881, TC 0.837, ET
Qamar et al
BraTS 2018
210 patients, to train and test
our model
3D Hyper-dense Connected
Convolutional Neural
Brain Tumour
Dice similarity
WT 0.87, ET 0.81, CT 0.84
0 Yan Hu et
BraTS 2017 285 training subjects, 46
validation subjects and 146 test
3D Deep neural network Intel Xeon 2.10
GTX 1080 Ti GPU,
Brain Tumour Dice similarity
WT 0.81, CT 0.69 and ET 0.55
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