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Effective 3D humerus and scapula extraction using low-contrast and high-shape-variability MR data
Author(s): Xiaoxiao He; Chaowei Tan; Yuting Qiao; Virak Tan; Dimitris Metaxas; Kang Li
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Paper Abstract

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.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530O (15 March 2019); doi: 10.1117/12.2513107
Show Author Affiliations
Xiaoxiao He, Rutgers, The State Univ. of New Jersey (United States)
Chaowei Tan, Rutgers, The State Univ. of New Jersey (United States)
Yuting Qiao, Rutgers, The State Univ. of New Jersey (United States)
Virak Tan, Rutgers, The State Univ. of New Jersey (United States)
Dimitris Metaxas, Rutgers, The State Univ. of New Jersey (United States)
Kang Li, Rutgers, The State Univ. of New Jersey (United States)


Published in SPIE Proceedings Vol. 10953:
Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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