Segment Any Bone in CT with Partial Supervision

Abstract

Automatic bone segmentation is a fundamental task supporting various clinical practices. Conventional methods in this field rely heavily on dense annotations, which incurs substantial labeling labor and expense. Recent efforts have been made to reduce the labeling workload through semi-supervised and weakly supervised learning. However, methods under these two paradigms usually assume that all objects of interest e.g., bones, are covered by labels. In this work, we explore a less studied problem setting that assumes only partially labeled bone CT data. To tackle the supervision bias brought by incomplete annotations, we design a three-stage learning method that automatically detects unlabeled bones while being robust to their various shape. Extensive experiments are conducted on the curated dataset to test the proposed method and promising performance is observed. To the best of our knowledge, this is the first work on the partially supervised bone segmentation problem.