Machine-based brain tumor segmentation can help doctors make better diagnoses. However, the complex structure of brain tumors and expensive pixel-level annotations present challenges for automatic tumor segmentation. In this paper, we propose a counterfactual generation framework that not only achieves exceptional brain tumor segmentation performance without the need for pixel-level annotations, but also provides explainability. Our framework effectively separates class-related features from class-unrelated features of the samples, and generate new samples that preserve identity features while altering class attributes by embedding different class-related features. We perform topological data analysis on the extracted class-related features and obtain a globally explainable manifold, and for each abnormal sample to be segmented, a meaningful normal sample could be effectively generated with the guidance of the rule-based paths designed within the manifold for comparison for identifying the tumor regions. We evaluate our proposed method on two datasets, which demonstrates superior performance of brain tumor segmentation. The code is available at https://github.com/xrt11/tumor-segmentation.

翻译：基于机器学习的脑肿瘤分割技术可辅助医生进行更精准的诊断。然而，脑肿瘤结构的复杂性及昂贵的像素级标注给自动肿瘤分割带来了挑战。本文提出一种反事实生成框架，该框架不仅能在无需像素级标注的情况下实现优异的脑肿瘤分割性能，同时提供可解释性。我们的框架能有效分离样本中与类别相关的特征和与类别无关的特征，并通过嵌入不同的类别相关特征，生成在保持身份特征的同时改变类别属性的新样本。我们对提取的类别相关特征进行拓扑数据分析，获得全局可解释的流形；对于待分割的异常样本，可基于该流形内设计的规则路径有效生成具有意义的正常样本进行对比，从而识别肿瘤区域。我们在两个数据集上评估了所提方法，结果证明了其在脑肿瘤分割方面的优越性能。代码发布于 https://github.com/xrt11/tumor-segmentation。