  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.
