An Automatic Brain Cortex Segmentation Technique Based on Dynamic Recalibration and Region Awareness
Abstract
1. Introduction
- A novel dual-module design (DRB + RAB) is proposed to enhance feature discrimination and structural awareness for accurate cortical segmentation.
- A lightweight, multi-plane segmentation strategy is employed to strike a balance between segmentation performance and computational efficiency.
- The training framework demonstrates strong generalization and robustness in small-sample scenarios.
- The proposed method offers a scalable and practical solution for clinical applications such as disease localization and neurosurgical navigation.
2. Materials and Methods
2.1. Datasets
2.2. Data Preprocessing
- (1)
- Skull stripping: brain and non-brain tissues were separated using grayscale thresholding and edge detection techniques to remove the skull, resulting in clean images for further brain analysis.
- (2)
- Normalization: A non-rigid registration algorithm was employed to align all brain images to a common standard space. This step mitigates individual variability and enables cross-subject comparison of brain structures.
- (3)
- Tissue segmentation: brain tissues (gray matter, white matter, and cerebrospinal fluid) were labeled using a Bayesian classification approach based on the Expectation-Maximization (EM) algorithm, ensuring efficiency and accuracy in subsequent segmentation processes.
- (4)
- White matter segmentation: a gradient-based watershed algorithm combined with morphological operations was applied to precisely segment white matter, providing fine-grained structural information for brain parcellation.
- (5)
- Cortical extraction: the cerebral cortex was extracted using algorithms based on gradient and geometric morphology, allowing accurate differentiation between cortical and subcortical structures.
- (6)
- Spherical registration: a spherical registration algorithm was used to map the cortical surface onto a standard spherical template, thereby reducing morphological differences across individuals.
- (7)
- Label annotation: finally, automatic labeling was performed using the DKT (Desikan–Killiany–Tourville) atlas to generate the training labels required for the model.
2.3. Methods
2.3.1. Cortical Structural Network Architecture
2.3.2. Dynamic Recalibration Block (DRB)
2.3.3. Region-Aware Block (RAB)
2.4. Loss Function
2.5. Evaluation Metrics
2.6. Experimental Setup
3. Experimental Results
3.1. Determination of the Number of RAB Modules
3.2. Model Convergence Results
3.2.1. Impact of Different Slice Orientations on Loss
3.2.2. Impact of Different Modules on Loss
3.3. Segmentation Accuracy Results
3.3.1. Comparative Results from Different Methods
Method | Dice (%) | Hausdorff Distance (mm) |
---|---|---|
U-Net [32] | 86.62 ± 2.81 * | 3.3657 ± 0.8257 * |
QuickNAT [25] | 87.70 ± 2.67 * | 2.8918 ± 0.7307 * |
FastSurfer [27] | 88.48 ± 4.38 * | 2.5700 ± 0.8962 * |
DRA-Net | 91.35 ± 3.76 | 2.1279 ± 0.8888 |
3.3.2. Generalization Results
3.3.3. Comparison with Mainstream Methods
Method | Dice (%) | Hausdorff Distance (mm) | |
---|---|---|---|
U-Net [32] | CNN | 71.83 ± 12.88 | 10.728 ± 7.1801 |
QuickNAT [25] | CNN | 75.84 ± 4.42 | 9.8983 ± 1.3562 |
FastSurfer [27] | CNN | 74.92 ± 12.01 | 9.2437 ± 0.5632 |
FreeSurfer [20] | Atlas-based | 74.75 ± 5.83 | 8.9852 ± 0.4936 |
FSL [14] | Atlas-based | 64.3 ± 0.29 | — |
JLF [36] | Atlas-based | 74.6 ± 0.90 | — |
F-CNN [34] | CNN | 57.9 ± 0.24 | — |
Naive U-Net [37] | CNN | 60.6 ± 0.60 | — |
SLANT-8 [35] | Transformer | 69.9 ± 1.40 | — |
SLANT-27 [35] | Transformer | 76.6 ± 0.80 | — |
SLANT-27 FT [35] | Transformer | 75.9 ± 1.70 | — |
DRA-Net | CNN | 77.99 ± 2.83 | 8.2571 ± 0.5009 |
3.4. Cortical Structure Segmentation Results
4. Discussion
4.1. Advantages of the Proposed Method
4.2. Analysis of DRB and RAB
4.3. Analysis of Segmentation Performance
4.4. Limitation
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Experiment | Dataset Name | Sample Size | Age Range (Years) | Mean Age ± SD (Years) | Male/ Female | Left-/Right-Handed |
---|---|---|---|---|---|---|
Training and Validation | NKI-RS | 22 | 20–40 | 26 ± 5.2 | 12/10 | 1/21 |
NKI-TRT | 20 | 19–60 | 31.4 ± 11.1 | 14/6 | 3/15 | |
MMRR | 21 | 22–61 | 31.8 ± 9.2 | 11/10 | 3/18 | |
HLN | 12 | 23–39 | 27.8 ± 4.6 | 6/6 | 0/12 | |
Colin27 | 1 | 33 | 33 | 1/0 | 0/1 | |
Twins | 2 | 41 | — | 0/2 | 0/2 | |
Independent Test | OASIS-TRT | 20 | 19–34 | 23.4 ± 3.9 | 8/12 | 0/20 |
Number of Layers | MIoU | Recall | Precision | Loss Total | Time (h) |
---|---|---|---|---|---|
1 | 0.66 ± 0.07 | 0.79 ± 0.06 | 0.79 ± 0.04 | 0.90 ± 0.07 | 38.14 |
2 | 0.73 ± 0.03 | 0.85 ± 0.02 | 0.83 ± 0.03 | 0.85 ± 0.03 | 41.02 |
3 | 0.72 ± 0.05 | 0.84 ± 0.05 | 0.83 ± 0.03 | 0.84 ± 0.06 | 41.55 |
4 | 0.73 ± 0.03 | 0.85 ± 0.02 | 0.83 ± 0.02 | 0.83 ± 0.03 | 42.01 |
5 | 0.73 ± 0.04 | 0.85 ± 0.03 | 0.83 ± 0.03 | 0.83 ± 0.03 | 42.43 |
6 | 0.74 ± 0.04 | 0.85 ± 0.02 | 0.84 ± 0.03 | 0.83 ± 0.03 | 46.94 |
View (Plane) | Mean Loss | Loss Volatility |
---|---|---|
Axial | 0.9018 | 0.0027 |
Coronal | 0.8630 | 0.0017 |
Sagittal | 0.7574 | 0.0015 |
ROI | Dice (%) | Hausdorff Distance (mm) | ROI | Dice (%) | Hausdorff Distance (mm) |
---|---|---|---|---|---|
Insula | 87.64 ± 3.40 | 4.7818 ± 1.6534 | Posterior cingulate | 78.83 ± 5.43 | 6.7194 ± 2.1258 |
Superior frontal | 84.72 ± 4.16 | 13.4316 ± 3.0392 | Postcentral | 78.76 ± 6.21 | 12.6653 ± 3.5746 |
Precentral | 84.62 ± 4.31 | 9.5087 ± 1.0761 | Inferior temporal | 78.71 ± 3.50 | 10.5309 ± 4.2501 |
Superior temporal | 83.50 ± 4.03 | 12.6089 ± 5.8031 | Middle frontal | 77.56 ± 4.78 | 11.8840 ± 2.4110 |
Precuneus | 83.00 ± 3.56 | 8.9933 ± 2.6247 | Fusiform | 76.43 ± 4.88 | 10.7908 ± 3.2435 |
Middle temporal | 81.27 ± 4.71 | 11.8217 ± 3.5855 | Orbitofrontal | 75.01 ± 3.84 | 9.1331 ± 1.0612 |
Parietal | 79.67 ± 4.83 | 11.6662 ± 1.9720 | Anterior cingulate | 74.78 ± 5.42 | 7.2973 ± 2.2701 |
Paracentral | 79.58 ± 4.25 | 9.0082 ± 2.7561 | Cuneus | 74.74 ± 4.72 | 9.7077 ± 2.4501 |
Lingual | 79.57 ± 4.70 | 7.5641 ± 2.4250 | Inferior frontal | 71.79 ± 6.32 | 9.9689 ± 2.2038 |
Parahippocampal | 79.29 ± 5.90 | 4.2071 ± 1.2654 | Entorhinal | 68.46 ± 6.65 | 6.1400 ± 1.0972 |
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Nan, J.; Fan, G.; Zhang, K.; Zhai, S.; Jin, X.; Li, D.; Yu, C. An Automatic Brain Cortex Segmentation Technique Based on Dynamic Recalibration and Region Awareness. Electronics 2025, 14, 3631. https://doi.org/10.3390/electronics14183631
Nan J, Fan G, Zhang K, Zhai S, Jin X, Li D, Yu C. An Automatic Brain Cortex Segmentation Technique Based on Dynamic Recalibration and Region Awareness. Electronics. 2025; 14(18):3631. https://doi.org/10.3390/electronics14183631
Chicago/Turabian StyleNan, Jiaofen, Gaodeng Fan, Kaifan Zhang, Shuyao Zhai, Xueqi Jin, Duan Li, and Chunlai Yu. 2025. "An Automatic Brain Cortex Segmentation Technique Based on Dynamic Recalibration and Region Awareness" Electronics 14, no. 18: 3631. https://doi.org/10.3390/electronics14183631
APA StyleNan, J., Fan, G., Zhang, K., Zhai, S., Jin, X., Li, D., & Yu, C. (2025). An Automatic Brain Cortex Segmentation Technique Based on Dynamic Recalibration and Region Awareness. Electronics, 14(18), 3631. https://doi.org/10.3390/electronics14183631