IEEE J Biomed Health Inform. 2023 Dec;27(12):5914-5925. doi: 10.1109/JBHI.2023.3321602. Epub 2023 Dec 5.
Brain tumor segmentation is a key step in brain cancer diagnosis. Segmentation of brain tumor sub-regions, including necrotic, enhancing, and edematous regions, can provide more detailed guidance for clinical diagnosis. Weakly supervised brain tumor segmentation methods have received much attention because they do not require time-consuming pixel-level annotations. However, existing weakly supervised methods focus on the segmentation of the entire tumor region while ignoring the challenging task of multi-label segmentation for the tumor sub-regions. In this article, we propose a weakly supervised approach to solve the multi-label brain tumor segmentation problem. To the best of our knowledge, it's the first end-to-end multi-label weakly supervised segmentation model applied to brain tumor segmentation. With well-designed loss functions and a contrastive learning pre-training process, our proposed Transformer-based segmentation method (WS-MTST) has the ability to perform segmentation of brain tumor sub-regions. We conduct comprehensive experiments and demonstrate that our method reaches the state-of-the-art on the popular brain tumor dataset BraTS (from 2018 to 2020).
脑肿瘤分割是脑癌诊断的关键步骤。对脑肿瘤亚区域（包括坏死、增强和水肿区域）的分割可以为临床诊断提供更详细的指导。由于不需要耗时的像素级注释，因此弱监督脑肿瘤分割方法受到了广泛关注。然而，现有的弱监督方法侧重于整个肿瘤区域的分割，而忽略了肿瘤亚区域的具有挑战性的多标签分割任务。在本文中，我们提出了一种弱监督方法来解决多标签脑肿瘤分割问题。据我们所知，这是第一个应用于脑肿瘤分割的端到端多标签弱监督分割模型。我们提出的基于 Transformer 的分割方法（WS-MTST）具有良好设计的损失函数和对比学习预训练过程，能够对脑肿瘤亚区域进行分割。我们进行了全面的实验，并证明我们的方法在流行的脑肿瘤数据集 BraTS（2018 年至 2020 年）上达到了最先进的水平。
