A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision

• URL: http://arxiv.org/abs/2010.12219v1
• Date: Fri, 23 Oct 2020 07:58:20 GMT
• Title: A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision
• Authors: Liyan Sun, Jianxiong Wu, Xinghao Ding, Yue Huang, Guisheng Wang and Yizhou Yu
• Abstract summary: We develop a semi-supervised learning framework based on a teacher-student fashion for organ and lesion segmentation. We show our model is robust to the quality of bounding box and achieves comparable performance compared with full-supervised learning methods.
• Score: 62.4773770041279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such learning framework is built on laborious manual annotation with restrict demands for expertise, leading to insufficient high-quality labels. To overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid labeling requirement and developed a semi-supervised learning framework based on a teacher-student fashion for organ and lesion segmentation with partial dense-labeled supervision and supplementary loose bounding-box supervision which are easier to acquire. Observing the geometrical relation of an organ and its inner lesions in most cases, we propose a hierarchical organ-to-lesion (O2L) attention module in a teacher segmentor to produce pseudo-labels. Then a student segmentor is trained with combinations of manual-labeled and pseudo-labeled annotations. We further proposed a localization branch realized via an aggregation of high-level features in a deep decoder to predict locations of organ and lesion, which enriches student segmentor with precise localization information. We validated each design in our model on LiTS challenge datasets by ablation study and showed its state-of-the-art performance compared with recent methods. We show our model is robust to the quality of bounding box and achieves comparable performance compared with full-supervised learning methods.

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