论文标题
跨模态脑肿瘤分割的自节义轮廓适应
Self-semantic contour adaptation for cross modality brain tumor segmentation
论文作者
论文摘要
在两个显着不同的领域之间进行无监督的域适应（UDA），学习高级语义一致性是一项至关重要但又具有挑战性的任务。适应。更具体地说，我们提出了一个多任务框架，以学习一个轮廓适应网络以及语义分割适应网络，该网络既采用磁共振成像（MRI）切片及其初始边缘图作为输入。此外，将最小化的自我注入量纳入了进一步提高分割性能。我们评估了BRATS2018数据库的框架，用于与竞争方法相比，脑肿瘤的跨模式分割，显示了我们方法的有效性和优越性。
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.
