Brain Tumor Segmentation
Brain tumor segmentation involves automatically identifying and outlining tumor regions in medical images, primarily MRI scans, to aid in diagnosis and treatment planning. Current research focuses on improving segmentation accuracy and robustness using advanced deep learning architectures like U-Net and its variants (e.g., Swin UNETR, nnU-Net), often incorporating attention mechanisms and multi-scale feature extraction to better handle the complex heterogeneity of brain tumors. These advancements are crucial for improving the speed and accuracy of clinical diagnosis, facilitating personalized treatment strategies, and potentially leading to better patient outcomes. Furthermore, significant effort is dedicated to addressing challenges like missing modalities and imbalanced datasets.
Papers
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand
José Gerardo Suárez-García Javier Miguel Hernández-López, Eduardo Moreno-Barbosa, Benito de Celis-Alonso
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation
Jianwei Lin, Jiatai Lin, Cheng Lu, Hao Chen, Huan Lin, Bingchao Zhao, Zhenwei Shi, Bingjiang Qiu, Xipeng Pan, Zeyan Xu, Biao Huang, Changhong Liang, Guoqiang Han, Zaiyi Liu, Chu Han
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation
Yao Zhang, Nanjun He, Jiawei Yang, Yuexiang Li, Dong Wei, Yawen Huang, Yang Zhang, Zhiqiang He, Yefeng Zheng
Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification
Dhananjay Joshi, Kapil Kumar Nagwanshi, Nitin S. Choubey, Naveen Singh Rajput