Effective 3D Humerus and Scapula Extraction using Low-contrast and High-shape-variability MR Data
This addresses the challenge of bone segmentation in medical imaging for clinical applications, but it is incremental as it builds on existing methods with a small dataset.
The paper tackled the problem of extracting 3D bone masks from low-contrast and high-shape-variability MR images for shoulder preoperative diagnosis, achieving better results compared to non-reinforced segmentation and a classical multi-atlas method.
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.