2020/01/01 by Shuailin Li, Chuyu Zhang, Xuming He · 2 citations
Computer Science · Engineering · #3D Shape Modeling and Analysis #Artificial intelligence #Computer science #Computer vision #Image Retrieval and Classification Techniques #Image segmentation #Medical Image Segmentation Techniques #Natural language processing #Pattern recognition (psychology) #Segmentation #cs.CV
paper · pdf · doi:10.1007/978-3-030-59710-8_54
published as MICCAI 2020. Lecture Notes in Computer Science, vol 12261. Springer, Cham · Appear in MICCAI2020
openalex publication_date 2020/01/01 · arxiv created 2020/07/21 · arxiv updated 2020/10/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Semi-supervised learning has attracted much attention in medical image segmentation due to challenges in acquiring pixel-wise image annotations, which is a crucial step for building high-performance deep learning methods. Most existing semi-supervised segmentation approaches either tend to neglect geometric constraint in object segments, leading to incomplete object coverage, or impose strong shape prior that requires extra alignment. In this work, we propose a novel shapeaware semi-supervised segmentation strategy to leverage abundant unlabeled data and to enforce a geometric shape constraint on the segmentation output. To achieve this, we develop a multi-task deep network that jointly predicts semantic segmentation and signed distance map(SDM) of object surfaces. During training, we introduce an adversarial loss between the predicted SDMs of labeled and unlabeled data so that our network is able to capture shape-aware features more effectively. Experiments on the Atrial Segmentation Challenge dataset show that our method outperforms current state-of-the-art approaches with improved shape estimation, which validates its efficacy. Code is available at https://github.com/kleinzcy/SASSnet.