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MSTransBTS—A Novel Integration of Mamba with Swin Transformer for 3D Brain Tumour Segmentation

by Jia Qin Ngu, Humaira Nisar and Chi-Yi Tsai

Mathematics 2025, 13(7), 1117; https://doi.org/10.3390/math13071117 - 28 Mar 2025

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This study focuses on the major challenges in ensuring the timely assessment and accurate diagnosis of brain tumors (BTs), which are essential for effective patient treatment. Hence, in this paper, a time-efficient, automated, and advanced deep learning (DL) solution, the Mamba Swin Transformer BT Segmentation (MSTransBTS) model, is introduced. This model employs the advanced Swin Transformer architecture, which is renowned for capturing long-range information and incorporates the latest Mamba approach for efficient long-range dependency modelling. Through meticulous customization and fine-tuning, the MSTransBTS achieves notable improvements in Dice scores, with scores of 89.53% for whole tumours (WTs), 80.09% for enhancing tumours (ETs), and 84.75% for tumour cores (TCs), resulting in an overall average Dice score of 84.79%. The employment of Test-Time Augmentation (TTA) further enhances performance and marks a significant advancement in BT segmentation accuracy. These findings not only address the critical need for timely assessment and diagnosis, but also emphasize the potential to enhance patient care through the automation of BT detection. By combining the features of Swin Transformer and Mamba techniques, this approach delivers a promising solution for accurate and efficient BT segmentation, which contributes to advancements in medical imaging. Full article

(This article belongs to the Special Issue Advanced Image Processing and Computational Intelligence: Methodologies and Applications)

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22 pages, 1618 KiB

Open AccessArticle

Cascade Residual Multiscale Convolution and Mamba-Structured UNet for Advanced Brain Tumor Image Segmentation

by Rui Zhou, Ju Wang, Guijiang Xia, Jingyang Xing, Hongming Shen and Xiaoyan Shen

Entropy 2024, 26(5), 385; https://doi.org/10.3390/e26050385 - 30 Apr 2024

Cited by 14 | Viewed by 4307

Abstract

In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model’s potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning. Full article

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