BTISS-WNET: Deep Learning-based Brain Tissue Segmentation using Spatio Temporal WNET - Athur Shaik Ali Gousia Banu; Sumit Hazra (Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA, 2026)
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