Challenges, optimization strategies, and future horizons of advanced deep learning approaches for brain lesion segmentation.
Authors
Affiliations (6)
Affiliations (6)
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518055, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China.
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518055, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen University, Shenzhen 518000, China; Institute of Urology, South China Hospital, Medicine School, Shenzhen University, Shenzhen 518000, China.
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China; School of Applied Technology, Shenzhen University, Shenzhen 518055, China.
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518055, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China; School of Applied Technology, Shenzhen University, Shenzhen 518055, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China. Electronic address: [email protected].
Abstract
Brain lesion segmentation is challenging in medical image analysis, aiming to delineate lesion regions precisely. Deep learning (DL) techniques have recently demonstrated promising results across various computer vision tasks, including semantic segmentation, object detection, and image classification. This paper offers an overview of recent DL algorithms for brain tumor and stroke segmentation, drawing on literature from 2021 to 2024. It highlights the strengths, limitations, current research challenges, and unexplored areas in imaging-based brain lesion classification based on insights from over 250 recent review papers. Techniques addressing difficulties like class imbalance and multi-modalities are presented. Optimization methods for improving performance regarding computational and structural complexity and processing speed are discussed. These include lightweight neural networks, multilayer architectures, and computationally efficient, highly accurate network designs. The paper also reviews generic and latest frameworks of different brain lesion detection techniques and highlights publicly available benchmark datasets and their issues. Furthermore, open research areas, application prospects, and future directions for DL-based brain lesion classification are discussed. Future directions include integrating neural architecture search methods with domain knowledge, predicting patient survival levels, and learning to separate brain lesions using patient statistics. To ensure patient privacy, future research is anticipated to explore privacy-preserving learning frameworks. Overall, the presented suggestions serve as a guideline for researchers and system designers involved in brain lesion detection and stroke segmentation tasks.