# Self-semantic contour adaptation for cross modality brain tumor segmentation Source: [Self-semantic contour adaptation for cross modality brain tumor segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:52:06.013Z Authors: Xiaofeng Liu, Fangxu Xing, Georges El Fakhri, Jonghye Woo Published: 2022-01-13T15:16:55.000Z ## Abstract Unsupervised domain adaptation (UDA) between two significantly disparate domains to learn high-level semantic alignment is a crucial yet challenging task.~To this end, in this work, we propose exploiting low-level edge information to facilitate the adaptation as a precursor task, which has a small cross-domain gap, compared with semantic segmentation.~The precise contour then provides spatial information to guide the semantic adaptation. More specifically, we propose a multi-task framework to learn a contouring adaptation network along with a semantic segmentation adaptation network, which takes both magnetic resonance imaging (MRI) slice and its initial edge map as input.~These two networks are jointly trained with source domain labels, and the feature and edge map level adversarial learning is carried out for cross-domain alignment. In addition, self-entropy minimization is incorporated to further enhance segmentation performance. We evaluated our framework on the BraTS2018 database for cross-modality segmentation of brain tumors, showing the validity and superiority of our approach, compared with competing methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Whole Tumor Segmentation on BraTS 2018 (test)](): Whole Tumor Segmentation; BraTS 2018 (test); DSC Average: 68.6. ## Original sources and implementation [Original paper]() [PDF]()