BTISS-WNET: Deep Learning-based Brain Tissue Segmentation using Spatio Temporal WNET
Abstract
Brain tissue segmentation (BTISS) from magnetic resonance imaging (MRI) is a critical process in neuroimaging, aiding in the analysis of brain morphology and facilitating accurate diagnosis and treatment of neurological disorders. A major challenge in BTISS is intensity inhomogeneity, which arises from variations in the magnetic field during image acquisition. This results in non-uniform intensities within the same tissue class, particularly affecting white matter (WM) segmentation. To address this problem, we propose an efficient deep learning-based framework, BTISS-WNET, for accurate segmentation of brain tissues. The main contribution of this work is the integration of a spatio-temporal segmentation strategy with advanced pre-processing and feature extraction to overcome intensity inconsistency and improve tissue differentiation. The process begins with skull stripping to eliminate non-brain tissues, followed by Empirical Wavelet Transform (EWT) for noise reduction and edge enhancement. Data augmentation techniques, including random rotation and flipping, are applied to improve model generalization. The preprocessed images are fed into Res-GoogleNet (RGNet) to extract deep semantic features. Finally, a Spatio-Temporal WNet is used for precise WM segmentation, leveraging spatial and temporal dependencies for improved boundary delineation. The proposed BTISS-WNET model achieves a segmentation accuracy of 99.32% for white matter. It also demonstrates improved accuracy of 1.76%, 18.23%, and 16.02% over DDSeg, BISON, and HMRF-WOA, respectively. In conclusion, BTISS-WNET provides a robust and high-accuracy framework for WM segmentation in MRI images, with promising applications in clinical neuroimaging. Future work will focus on validating the model using real clinical datasets and extending it to multi-tissue and multi-modal MRI segmentation
Downloads
References
L. Wu, S. Wang, J. Liu, L. Hou, N. Li, F. Su, X. Yang, W. Lu, J. Qiu, M. Zhang, & L. Song, “A survey of MRI-based brain tissue segmentation using deep learning,” Complex & Intelligent Systems, vol. 11, no. 1, pp. 1-16, 2025. doi: 10.1007/s40747-024-01639-1
Q.U. Mastoi, S. Latif, S. Brohi, J. Ahmad, A. Alqhatani, M.S. Alshehri, A. Al Mazroa, & R. Ullah, “Explainable AI in medical imaging: an interpretable and collaborative federated learning model for brain tumor classification,” Frontiers in Oncology, vol. 15, pp. 1535478-1535478, 2025. doi: 10.3389/fonc.2025.1535478
K. Sailunaz, S. Alhajj, T. Özyer, J. Rokne, and R. Alhajj, “A survey on brain tumor image analysis,” Medical & Biological Engineering & Computing, vol. 62, no. 1, pp. 1-45, 2024. doi: 10.1007/s11517-023-02873-4
Z. Liu, L. Tong, L. Chen, Z. Jiang, F. Zhou, Q. Zhang, X. Zhang, Y. Jin, and H. Zhou, “Deep learning-based brain tumor segmentation: a survey,” Complex & intelligent systems, vol. 9, no. 1, pp. 1001-1026, 2023. doi: 10.1007/s40747-022-00815-5
R. Palaniappan, and S. Rathinavelayutham, “Digital Twin-Assisted Deep Learning with Model Fusion for Detecting Multiple Sclerosis in MRI Modalities,” Journal of Data Science and Intelligent Systems, 2025. doi: 10.47852/bonviewjdsis52024391
C. John Clementsingh and S. Sumathi, “Face Regeneration and Recognition using Deep Learning based Sift-Hog Assisted Gan Model”, International Journal of Data Science and Artificial Intelligence, vol. 02, no.05, pp. 142-148, 2024.
Cheepurupalli Raghuram, V. S. R. K. Raju Dandu, and DR. B. Jaison, “Hybridization of Dilated Cnn with Attention Link Net for Brain Cancer Classification,” International Journal of Data Science and Artificial Intelligence, vol. 02, no. 02, pp. 35-42, 2024.
R. Sundarasekar, and A. Appathurai, “FMTM-feature-map-based transform model for brain image segmentation in tumor detection,” Network Computing Neural Systems, vol. 34, no. 1-2, pp. 1-25, 2023. doi: 10.1080/0954898x.2022.2110620
X. Huang, Y. Liu, Y. Li, K. Qi, A. Gao, B. Zheng, D. Liang, and X. Long, “Deep learning-based multiclass brain tissue segmentation in fetal MRIs,” Sensors, vol. 23, no. 2, pp.655, 2023. doi: 10.3390/s23020655
R. Palaniappan, and S. Rathinavelayutham, “Digital Twin-Assisted Deep Learning with Model Fusion for Detecting Multiple Sclerosis in MRI Modalities,” Journal of Data Science and Intelligent Systems, 2025. doi: 10.47852/bonviewjdsis52024391
R. Abirami, and P. Krishna Kumar, “Deep Learning Model for Accurate Brain Tumor Detection Using CT and MRI Imaging,” International Journal of System Design and Computing, vol. 02, no. 01, pp. 26-31, 2024.
B. Thyreau, Y. Tatewaki, L. Chen, Y. Takano, N. Hirabayashi, Y. Furuta, J. Hata, S. Nakaji, T. Maeda, M. Noguchi‐Shinohara, and M. Mimura, “Higher‐resolution quantification of white matter hypointensities by large‐scale transfer learning from 2D images on the JPSC‐AD cohort,” Human Brain Mapping, vol. 43, no. 13, pp. 3998-4012, 2022. doi: 10.1002/hbm.25899
A. Appathurai, A. S. I. Tinu, and M. Narayanaperumal, “Meg and Pet Images-Based Brain Tumor Detection Using Kapur’s Otsu Segmentation and Sooty Optimized Mobilenet Classification,” Revue Roumaine des Sciences Techniques — Série Électrotechnique Et Énergétique, vol. 69, no. 3, pp. 359-364, 2024. doi: 10.59277/rrst-ee.2024.69.3.18
T. Magadza, and S. Viriri, “Deep learning for brain tumor segmentation: a survey of state-of-the-art,” Journal of Imaging, vol. 7, no. 2, pp. 19, 2021. doi: 10.3390/jimaging7020019
R. Jin, D. Li, D. Xiang, L. Zhang, H. Zhou, F. Shi, W. Zhu, J. Cai, T. Peng, and X. Chen, “AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review,” arXiv preprint arXiv:2407.06612, 2024. doi: 10.21037/qims-24-723
N. Yamanakkanavar, and B. Lee, “Using a patch-wise m-net convolutional neural network for tissue segmentation in brain mri images,” IEEE Access, vol. 8, pp. 120946-120958, 2020. doi: 10.1109/access.2020.3006317
M. Veluchamy, and B. Subramani, “Brain tissue segmentation for medical decision support systems,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 2, pp. 1851-1868, 2021. doi: 10.1007/s12652-020-02257-8
A. Clerigues, S. Valverde, J. Salvi, A. Oliver, and X. Lladó, “Minimizing the effect of white matter lesions on deep learning-based tissue segmentation for brain volumetry,” Computerized Medical Imaging and Graphics, vol. 103, pp. 102157, 2023. doi: 10.1016/j.compmedimag.2022.102157
G. Park, J. Hong, B.A. Duffy, J.M. Lee, and H. Kim, “White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds,” Neuroimage, vol. 237, pp. 118140, 2021. doi: 10.1016/j.neuroimage.2021.118140
N. Yamanakkanavar, and B. Lee, “A novel M-SegNet with global attention CNN architecture for automatic segmentation of brain MRI,” Computers in Biology and Medicine, vol. 136, pp. 104761, 2021. doi: 10.1016/j.compbiomed.2021.104761
Z. Rieu, J. Kim, R.E. Kim, M. Lee, M.K. Lee, S.W. Oh, S.M. Wang, N.Y. Kim, D.W. Kang, H.K. Lim, and D. Kim, “Semi-supervised learning in medical MRI segmentation: brain tissue with white matter hyperintensity segmentation using FLAIR MRI,” Brain Sciences, vol. 11, no. 6, pp. 720, 2021. doi: 10.3390/brainsci11060720
S. Kollem, “An efficient method for MRI brain tumor tissue segmentation and classification using an optimized support vector machine,” Multimedia Tools Applications, vol. 83, no. 26, pp. 68487-68519, 2024. doi: 10.1007/s11042-024-18233-9
S. Gudise, K. Giri Babu, & T. Satya Savithri, “An advanced fuzzy C-Means algorithm for the tissue segmentation from brain magnetic resonance images in the presence of noise and intensity inhomogeneity,” The Imaging Science Journal, vol. 72, no. 4, pp. 520-539, 2024. doi: 10.1080/13682199.2023.2210400
S. Mohapatra, G.K. Pati, M. Mishra, and T. Swarnkar, “Gastrointestinal abnormality detection and classification using empirical wavelet transform and deep convolutional neural network from endoscopic images,” Ain Shams Engineering Journal, vol. 14, no. 4, pp. 101942, 2023. doi: 10.1007/s12539-021-00417-8
V. Chamola, and P. Narang, “AGD-Net: Attention-Guided Dense Inception U-Net for Single-Image Dehazing,” 2023. doi: 10.1007/s12559-023-10244-2
V. Rathinam, R. Arunagiri, V. Krishnasamy, and S. Rajendran, “Tri-level lung cancer classification via deep learning based GoogleNet with computed tomography images,” Bulletin of Electrical Engineering and Informatics, vol. 14, no. 3, pp. 2198-2208, 2025. doi: 10.11591/eei.v14i3.9258
M. Talele, and R. Jain, “A Comparative Analysis of CNNs and ResNet50 for Facial Emotion Recognition,” Engineering, Technology & Applied Science Research, vol. 15, no. 2, pp. 20693-20701, 2025. doi: 10.48084/etasr.9849
W. Xu, Y.L. Fu, and D. Zhu, “ResNet and its application to medical image processing: Research progress and challenges,” Computer Methods and Programs in Biomedicine, vol. 240, pp. 107660, 2023. doi: 10.1016/j.cmpb.2023.107660
M. Grammatikopoulou, R. Sanchez-Matilla, F. Bragman, D. Owen, L. Culshaw, K. Kerr, D. Stoyanov, and I. Luengo, “A spatio-temporal network for video semantic segmentation in surgical videos,” International Journal of Computer Assisted Radiology and Surgery, vol. 19, no. 2, pp. 375-382, 2024. doi: 10.1007/s11548-023-02971-6
B.M. Anderson, B. Rigaud, Y.M. Lin, A.K.Jones, H.C. Kang, B.C. Odisio, and K.K. Brock, “Automated segmentation of colorectal livermetastasis and liver ablation on contrast-enhanced CT images,” Frontiers inOncology, vol. 12, pp. 886517, 2022. doi: 10.3389/fonc.2022.886517
Alqhtani, S.M., Soomro, T.A., Shah, A.A., Memon, A.A., Irfan, M., Rahman, S., Jalalah, M., Almawgani, A.H. and Eljak, L.A.B., 2024. Improved brain tumor segmentation and classification in brain MRI with FCM-SVM: a diagnostic approach. IEEE Access, vol. 12, pp.61312-61335.
Almufareh, M.F., Imran, M., Khan, A., Humayun, M. and Asim, M., 2024. Automated brain tumor segmentation and classification in MRI using YOLO-based deep learning. IEEE Access, vol. 12, pp.16189-16207.
Y. Ghasemipour, and M. Niazi, 2025. Image segmentation enhancement using aggregated kernel graph cut. Signal Processing and Renewable Energy (SPRE), vol. 9, no. 1, 2025. doi: 10.1007/s11760-023-02546-7
V.S.D. Tejaswi, and V. Rachapudi, “Computer-aided diagnosis of liver cancer with improved SegNet and deep stacking ensemble model,” Computational Biology and Chemistry, vol. 113, pp. 108243, 2024. doi: 10.1016/j.compbiolchem.2024.108243
Z. Wang, S. Fu, H. Zhang, C. Wang, C. Xia, P. Hou, C. Shun, and G. Shun, “Dual-branch dynamic hierarchical U-Net with multi-layer space fusion attention for medical image segmentation,” Scientific Reports, vol. 15, no. 1, pp. 8194, 2025. doi: 10.1038/s41598-025-92715-0
F. Turk, and M. Kılıçaslan, “Lung image segmentation with improved U-Net, V-Net and Seg-Net techniques,” PeerJ Computer Science, vol. 11, pp. e2700, 2025. doi: 10.7717/peerj-cs.2700
T. Chauhan, V. Katkar, and K. Vaghela, “Corn leaf disease detection using RegNet, KernelPCA and XGBoost classifier,” In International Conference on Advancements in Smart Computing and Information Security, pp. 346-361, 2022. doi: 10.1007/978-3-031-23092-9_28
T.S. Priya, and T. Ramaprabha, “Resnet based feature extraction with decision tree classifier for classificaton of mammogram images,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 2, pp. 1147-1153, 2021. doi: 10.17762/turcomat.v12i2.1136
A.W. Al-funjan, H.M. Al Abboodi, N.A. Hamza, W.M.S. Abedi, and A.H. Abdullah, “A Lightweight Deep Learning-Based Ocular Disease Prediction Model Using Squeeze-and-Excitation Network Architecture with MobileNet Feature Extraction,” International Journal of Intelligent Engineering & Systems, vol. 17, no. 4, 2024. doi: 10.22266/ijies2024.0831.19
A.W. Al-funjan, H.M. Al Abboodi, N.A. Hamza, W.M.S. Abedi, and A.H. Abdullah, “A Lightweight Deep Learning-Based Ocular Disease Prediction Model Using Squeeze-and-Excitation Network Architecture with MobileNet Feature Extraction,” International Journal of Intelligent Engineering & Systems, vol. 17, no. 4, 2024. doi: 10.22266/ijies2024.0831.19
F. Zhang, A. Breger, K.I.K. Cho, L. Ning, C.F. Westin, L.J. O’Donnell, and O. Pasternak, “Deep learning-based segmentation of brain tissue from diffusion MRI,” NeuroImage, vol. 233, pp. 117934, 2021. doi: 10.1016/j.neuroimage.2021.117934
M. Dadar, and D.L. Collins, “BISON: Brain tissue segmentation pipeline using T1‐weighted magnetic resonance images and a random forest classifier,” Magnetic Resonance in Medicine, vol. 85, no. 4, pp. 1881-1894, 2021. doi: 10.1002/mrm.28547
A. Daoudi, and S. Mahmoudi, “Enhancing Brain Segmentation in MRI through Integration of Hidden Markov Random Field Model and Whale Optimization Algorithm,” Computers, vol. 13, no. 5, pp. 124, 2024. doi: 10.3390/computers13050124.
L. Wu, S. Wang, J. Liu, L. Hou, N. Li, F. Su, X. Yang, W. Lu, J. Qiu, M. Zhang, L. Song, “A survey of MRI-based brain tissue segmentation using deep learning,” Complex & Intelligent Systems, vol. 11, no. 1, pp. 1–16, 2025. doi: 10.1007/s40747-024-01639-1
Z. Liu, L. Tong, L. Chen, Z. Jiang, F. Zhou, Q. Zhang, X. Zhang, Y. Jin, H. Zhou, “Deep learning-based brain tumor segmentation: a survey,” Complex & Intelligent Systems, vol. 9, no. 1, pp. 1001–1026, 2023. doi: 10.1007/s40747-022-00815-5
T. Magadza, S. Viriri, “Deep learning for brain tumor segmentation: a survey of state-of-the-art,” Journal of Imaging, vol. 7, no. 2, pp. 19, 2021. doi: 10.3390/jimaging7020019
R. Palaniappan, S. Rathinavelayutham, “Digital Twin-Assisted Deep Learning with Model Fusion for Detecting Multiple Sclerosis in MRI Modalities,” Journal of Data Science and Intelligent Systems, 2025. doi: 10.47852/bonviewjdsis52024391
X. Huang, Y. Liu, Y. Li, K. Qi, A. Gao, B. Zheng, D. Liang, X. Long, “Deep learning-based multiclass brain tissue segmentation in fetal MRIs,” Sensors, vol. 23, no. 2, pp. 655, 2023. doi: 10.3390/s23020655
B. Thyreau, Y. Tatewaki, L. Chen, Y. Takano, N. Hirabayashi, Y. Furuta, J. Hata, S. Nakaji, T. Maeda, M. Noguchi‐Shinohara, M. Mimura, “Higher‐resolution quantification of white matter hypointensities by large‐scale transfer learning from 2D images on the JPSC‐AD cohort,” Human Brain Mapping, vol. 43, no. 13, pp. 3998–4012, 2022. doi: 10.1002/hbm.25899
Copyright (c) 2025 Athur Shaik Ali Gousia Banu, Sumit Hazra, Razia Alangir Banu
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
.png)
.png)
.png)
.png)
.png)
