Research on Automatic Segmentation Algorithm of Brain Tumor Image Based on Multi-sequence Self-supervised Fusion in Complex Scenes

Brain tumors play a crucial role in medical diagnosis and treatment planning. Extracting tumor information from MRI images is essential but can be challenging due to the limitations and intricacy of manual delineation. This paper presents a brain tumor image segmentation framework that addresses these challenges by leveraging multiple sequence information. The framework consists of encoder, decoder, and data fusion modules. The encoder incorporates Bi-ConvLSTM and Transformer models, enabling comprehensive utilization of both local and global details in each sequence. The decoder module employs a lightweight MLP architecture. Additionally, we propose a data fusion module that integrates self-supervised multi-sequence segmentation results. This module learns the weights of each sequence prediction result in an end-to-end manner, ensuring robust fusion results. Experimental validation on the BRATS 2018 dataset demonstrates the excellent performance of the proposed automatic segmentation framework for brain tumor images. Comparative analysis with other multi-sequence fusion segmentation models reveals that our framework achieves the highest Dice score in each region. To provide a more comprehensive background, it is important to highlight the significance of brain tumors in medical diagnosis and treatment planning. Brain tumors can have serious implications for patients, affecting their overall health and well-being. Accurate segmentation of brain tumors from MRI images is crucial for assessing tumor size, location, and characteristics, which in turn informs treatment decisions and prognosis. Currently, manual delineation of brain tumors from MRI images is a time-consuming and labor-intensive process prone to inter-observer variability. Automating this segmentation task using advanced image processing techniques can significantly improve efficiency and reliability.