Self-semantic contour adaptation for cross modality brain tumor segmentation

• URL: http://arxiv.org/abs/2201.05022v1
• Date: Thu, 13 Jan 2022 15:16:55 GMT
• Title: Self-semantic contour adaptation for cross modality brain tumor segmentation
• Authors: Xiaofeng Liu, Fangxu Xing, Georges El Fakhri, Jonghye Woo
• Abstract summary: We propose exploiting low-level edge information to facilitate the adaptation as a precursor task. The precise contour then provides spatial information to guide the semantic adaptation. We evaluate our framework on the BraTS2018 database for cross-modality segmentation of brain tumors.
• Score: 13.260109561599904
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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