SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans
Abstract
:1. Introduction
- We propose “lightweight SM-SegNet,” a fully automatic brain tissue segmentation on MRI, using a multi-scale deep network integrated with a fire module.
- Our SM-SegNet architecture represents an end-to-end training network that applies an M-shape convolutional network with multi-scale side layers at the input side to learn discriminative information; the upsampling layer at the output side provides deep supervision.
- The proposed long skip connections stabilize the gradient updates in the proposed architecture, improving the optimization convergence speed.
- The encoder and decoder (designed with fire modules) reduce the number of parameters and the computational complexity, resulting in a more efficient network for brain MRI segmentation.
- We propose using a uniform division of patches from brain MRI scans to enhance local details in the trained model; this minimizes the loss of semantic features.
2. Materials and Methods
3. Proposed Methodology
3.1. Outline of the Proposed Method
3.2. Outline of the Proposed Method
3.2.1. Encoder Block
3.2.2. Decoder Block
3.2.3. Classification Layer
3.2.4. Fire Module
3.2.5. Training of OASIS and IBSR Datasets
4. Experimental Results and Analysis
4.1. Materials
4.1.1. OASIS Dataset
4.1.2. IBSR Dataset
4.2. Experimental Setups
4.3. Results for OASIS and IBSR Datasets
4.4. Ablation Study
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Categories | Number of Subjects | |
---|---|---|
OASIS | IBSR | |
Males | 160 | 14 |
Females | 256 | 4 |
Total | 416 | 18 |
OASIS | |||||||||
---|---|---|---|---|---|---|---|---|---|
Axial Plane | |||||||||
Methods | WM | GM | CSF | ||||||
DSC | JI | HD | DSC | JI | HD | DSC | JI | HD | |
SegNet [19] | 0.087 | 0.096 | 0.077 | 0.069 | 0.089 | 0.053 | 0.048 | 0.068 | 0.079 |
U-net [20] | 0.059 | 0.068 | 0.064 | 0.048 | 0.061 | 0.046 | 0.076 | 0.090 | 0.039 |
M-net [21] | 0.046 | 0.057 | 0.023 | 0.055 | 0.072 | 0.077 | 0.044 | 0.065 | 0.043 |
U-net++ [34] | 0.053 | 0.062 | 0.048 | 0.035 | 0.048 | 0.025 | 0.039 | 0.052 | 0.036 |
CE-Net [36] | 0.039 | 0.044 | 0.050 | 0.042 | 0.057 | 0.044 | 0.043 | 0.063 | 0.061 |
M-SegNet [39] | 0.030 | 0.053 | 0.041 | 0.033 | 0.048 | 0.026 | 0.029 | 0.042 | 0.032 |
Proposed | 0.032 | 0.040 | 0.028 | 0.027 | 0.034 | 0.019 | 0.021 | 0.036 | 0.015 |
Coronal Plane | |||||||||
SegNet [19] | 0.058 | 0.065 | 0.023 | 0.044 | 0.068 | 0.053 | 0.056 | 0.074 | 0.084 |
U-net [20] | 0.043 | 0.059 | 0.042 | 0.057 | 0.069 | 0.042 | 0.063 | 0.081 | 0.073 |
M-net [21] | 0.048 | 0.060 | 0.066 | 0.021 | 0.032 | 0.038 | 0.034 | 0.051 | 0.032 |
U-net++ [34] | 0.066 | 0.073 | 0.047 | 0.042 | 0.057 | 0.044 | 0.048 | 0.059 | 0.043 |
CE-Net [36] | 0.031 | 0.046 | 0.076 | 0.038 | 0.050 | 0.071 | 0.039 | 0.053 | 0.050 |
M-SegNet [39] | 0.024 | 0.038 | 0.046 | 0.024 | 0.036 | 0.066 | 0.032 | 0.048 | 0.036 |
Proposed | 0.023 | 0.032 | 0.038 | 0.033 | 0.046 | 0.054 | 0.044 | 0.062 | 0.028 |
Sagittal plane | |||||||||
SegNet [19] | 0.054 | 0.066 | 0.027 | 0.083 | 0.095 | 0.033 | 0.040 | 0.057 | 0.088 |
U-net [20] | 0.058 | 0.070 | 0.030 | 0.074 | 0.090 | 0.026 | 0.058 | 0.073 | 0.079 |
M-net [21] | 0.038 | 0.045 | 0.046 | 0.083 | 0.094 | 0.026 | 0.029 | 0.046 | 0.082 |
U-net++ [34] | 0.060 | 0.072 | 0.031 | 0.038 | 0.049 | 0.019 | 0.041 | 0.063 | 0.041 |
CE-Net [36] | 0.043 | 0.064 | 0.020 | 0.025 | 0.037 | 0.034 | 0.051 | 0.062 | 0.055 |
M-SegNet [39] | 0.029 | 0.047 | 0.035 | 0.021 | 0.035 | 0.042 | 0.036 | 0.047 | 0.027 |
Proposed | 0.035 | 0.044 | 0.038 | 0.044 | 0.056 | 0.035 | 0.058 | 0.073 | 0.045 |
Axial Plane | |||||||||
SegNet [19] | 0.036 | 0.042 | 0.65 | 0.049 | 0.058 | 0.91 | 0.079 | 0.095 | 0.46 |
U-net [20] | 0.022 | 0.034 | 0.51 | 0.027 | 0.038 | 0.51 | 0.062 | 0.079 | 0.31 |
M-net [21] | 0.043 | 0.051 | 0.39 | 0.053 | 0.068 | 0.65 | 0.039 | 0.048 | 0.18 |
U-net++ [34] | 0.085 | 0.096 | 0.36 | 0.037 | 0.049 | 0.29 | 0.058 | 0.072 | 0.64 |
CE-Net [36] | 0.055 | 0.073 | 0.84 | 0.068 | 0.083 | 0.38 | 0.037 | 0.054 | 0.93 |
M-SegNet [39] | 0.038 | 0.049 | 0.64 | 0.055 | 0.028 | 0.47 | 0.032 | 0.055 | 0.24 |
Proposed | 0.042 | 0.054 | 0.57 | 0.026 | 0.040 | 0.92 | 0.026 | 0.039 | 0.79 |
Coronal Plane | |||||||||
SegNet [19] | 0.043 | 0.052 | 0.82 | 0.037 | 0.052 | 0.84 | 0.064 | 0.086 | 0.75 |
U-net [20] | 0.035 | 0.046 | 0.67 | 0.044 | 0.056 | 0.38 | 0.028 | 0.043 | 0.47 |
M-net [21] | 0.046 | 0.058 | 0.21 | 0.035 | 0.043 | 0.19 | 0.075 | 0.093 | 0.25 |
U-net++ [34] | 0.059 | 0.073 | 0.39 | 0.063 | 0.078 | 0.24 | 0.048 | 0.067 | 0.39 |
CE-Net [36] | 0.054 | 0.066 | 0.21 | 0.049 | 0.068 | 0.93 | 0.056 | 0.072 | 0.20 |
M-SegNet [39] | 0.026 | 0.043 | 0.42 | 0.071 | 0.040 | 0.36 | 0.033 | 0.047 | 0.52 |
Proposed | 0.039 | 0.051 | 0.43 | 0.019 | 0.032 | 0.67 | 0.022 | 0.034 | 0.12 |
Sagittal Plane | |||||||||
SegNet [19] | 0.036 | 0.048 | 0.61 | 0.073 | 0.089 | 0.76 | 0.073 | 0.092 | 0.41 |
U-net [20] | 0.049 | 0.062 | 0.37 | 0.036 | 0.045 | 0.21 | 0.071 | 0.089 | 0.15 |
M-net [21] | 0.026 | 0.038 | 0.14 | 0.045 | 0.062 | 0.06 | 0.056 | 0.073 | 0.09 |
U-net++ [34] | 0.033 | 0.045 | 0.54 | 0.063 | 0.081 | 0.22 | 0.049 | 0.070 | 0.44 |
CE-Net [36] | 0.054 | 0.065 | 0.66 | 0.051 | 0.077 | 0.55 | 0.033 | 0.045 | 0.37 |
M-SegNet [39] | 0.032 | 0.049 | 0.52 | 0.029 | 0.042 | 0.31 | 0.020 | 0.035 | 0.32 |
Proposed | 0.035 | 0.053 | 0.36 | 0.028 | 0.043 | 0.18 | 0.024 | 0.039 | 0.66 |
Methods | WM | GM | CSF | Parameters | Training Time | |||
---|---|---|---|---|---|---|---|---|
DSC | JI | DSC | JI | DSC | JI | |||
M-SegNet only | 0.94 | 0.89 | 0.95 | 0.90 | 0.94 | 0.89 | 5,468,932 | 3.09 h |
SM-SegNet without long skip | 0.95 | 0.90 | 0.94 | 0.89 | 0.94 | 0.89 | 835,770 | 1.50 h |
M-SegNet with long skip | 0.96 | 0.92 | 0.95 | 0.90 | 0.94 | 0.89 | 5,468,944 | 3.15 h |
Combined | 0.97 | 0.94 | 0.96 | 0.92 | 0.95 | 0.90 | 835,776 | 1.30 h |
Patch Size | WM | GM | CSF | Training Time | ||||||
---|---|---|---|---|---|---|---|---|---|---|
DSC | JI | HD | DSC | JI | HD | DSC | JI | HD | ||
3232 | 0.98 | 0.96 | 3.10 | 0.97 | 0.94 | 3.05 | 0.96 | 0.92 | 3.15 | 13.90 h |
6464 | 0.98 | 0.96 | 3.16 | 0.97 | 0.94 | 3.09 | 0.95 | 0.90 | 3.19 | 6.50 h |
128128 | 0.97 | 0.94 | 3.25 | 0.96 | 0.92 | 3.15 | 0.95 | 0.90 | 3.22 | 1.30 h |
No. | Parameters | Overlapping Patches | Non-Overlapping Patches |
---|---|---|---|
1 | Input size | 128 | 128 |
2 | Training set | 120 subjects | 120 subjects |
3 | Testing set | 30 subjects | 30 subjects |
4 | # of patches | 32 (stride: 8 pixels) | 4 |
5 | # of epochs | 10 | 10 |
6 | DSC | 0.97 | 0.96 |
7 | JI | 0.94 | 0.92 |
8 | Training time | 28.5 h | 1.3 h |
Methods | DSC and JI | Datasets | Features | |||
---|---|---|---|---|---|---|
GM | WM | CSF | ||||
1 | Bao [67] | 0.85 | 0.82 | 0.82 | IBSR | Multi-scale structured CNN |
2 | Khagi [68] | 0.74 | 0.81 | 0.72 | OASIS | Simplified SegNet architecture |
3 | Shakeri [69] | 0.82 | 0.82 | 0.82 | IBSR | Multi-label segmentation using fully CNN (FCNN) |
4 | Dolz [70] | 0.90 | 0.90 | 0.90 | IBSR | 3D FCNN |
5 | Proposed | 0.96 | 0.97 | 0.95 | OASIS | Patch-wise-based SM-SegNet architecture |
0.92 | 0.90 | 0.83 | IBSR |
Sets | Training (Subject #) | Test (Subject #) | Parameter | GM | WM | CSF |
---|---|---|---|---|---|---|
TestSet0 | 6–17 | 0–5 | DSC | 0.91 | 0.88 | 0.79 |
JI | 0.83 | 0.79 | 0.65 | |||
TestSet1 | 0–5 and 12–7 | 6–11 | DSC | 0.90 | 0.90 | 0.80 |
JI | 0.82 | 0.82 | 0.67 | |||
TestSet2 | 0–11 | 12–17 | DSC | 0.92 | 0.89 | 0.83 |
JI | 0.85 | 0.80 | 0.71 |
Metrics | SegNet vs. Proposed | U-Net vs. Proposed | M-Net vs. Proposed | U-Net++ vs. Proposed | CE-Net vs. Proposed |
---|---|---|---|---|---|
DSC | 0.018 | 0.026 | 0.029 | 0.034 | 0.038 |
Model | Training Set | Test Set | DSC | ||
---|---|---|---|---|---|
GM | WM | CSF | |||
Proposed | IBSR—18 Subjects | OASIS—15 Subjects | 0.81 | 0.88 | 0.63 |
OASIS—50 Subjects | IBSR—18 Subjects | 0.60 | 0.67 | 0.54 |
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Yamanakkanavar, N.; Choi, J.Y.; Lee, B. SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans. Sensors 2022, 22, 5148. https://doi.org/10.3390/s22145148
Yamanakkanavar N, Choi JY, Lee B. SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans. Sensors. 2022; 22(14):5148. https://doi.org/10.3390/s22145148
Chicago/Turabian StyleYamanakkanavar, Nagaraj, Jae Young Choi, and Bumshik Lee. 2022. "SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans" Sensors 22, no. 14: 5148. https://doi.org/10.3390/s22145148
APA StyleYamanakkanavar, N., Choi, J. Y., & Lee, B. (2022). SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans. Sensors, 22(14), 5148. https://doi.org/10.3390/s22145148