  For the initial shoulder preoperative diagnosis, it is essential to obtain a
three-dimensional (3D) bone mask from medical images, e.g., magnetic resonance
(MR). However, obtaining high-resolution and dense medical scans is both costly
and time-consuming. In addition, the imaging parameters for each 3D scan may
vary from time to time and thus increase the variance between images.
Therefore, it is practical to consider the bone extraction on low-resolution
data which may influence imaging contrast and make the segmentation work
difficult. In this paper, we present a joint segmentation for the humerus and
scapula bones on a small dataset with low-contrast and high-shape-variability
3D MR images. The proposed network has a deep end-to-end architecture to obtain
the initial 3D bone masks. Because the existing scarce and inaccurate
human-labeled ground truth, we design a self-reinforced learning strategy to
increase performance. By comparing with the non-reinforced segmentation and a
classical multi-atlas method with joint label fusion, the proposed approach
obtains better results.
