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Medical imaging is rapidly taking a predominant position for the diagnosis and treatment of lower back abnormalities. Magnetic resonance imaging (MRI) is a primary tool for detecting anatomical and functional abnormalities in the intervertebral disc (IVD) and provides valuable data for both diagnosis and research. Deep learning methods perform well in computer visioning when labeled general image training data are abundant. In the practice of medical images, the labeled data or the segmentation data are produced manually. However, manual medical image segmentation leads to two main issues: much time is needed for delineation, and reproducibility is called into question. To handle this problem, we developed an automated approach for IVD instance segmentation that can utilize T1 and T2 images during this study to handle data limitation problems and computational time problems and improve the generalization of the algorithm. This method builds upon mask-RCNN; we proposed a multistage optimization mask-RCNN (MOM-RCNN) for deep learning segmentation networks. We used a multi-optimization training system by utilizing stochastic gradient descent and adaptive moment estimation (Adam) with T1 and T2 in MOM-RCNN. The proposed method showed a significant improvement in processing time and segmentation results compared to previous commonly used segmentation methods. We evaluated the results using several different key performance measures. We obtain the Dice coefficient (99%). Our method can define the IVD\u2019s segmentation as much as 88% (sensitivity) and recognize the non-IVD as much as 98% (specificity). The results also obtained increasing precision (92%) with a low global consistency error (0.03), approaching 0 (the best possible score). On the spatial distance measures, the results show a promising reduction from 0.407 \u00b1 0.067\u00a0mm in root mean square error to 0.095 \u00b1 0.026\u00a0mm, Hausdorff distance from 12.313 \u00b1 3.015 to 5.155 \u00b1 1.561\u00a0mm, and average symmetric surface distance from 1.944 \u00b1 0.850 to 0.49 \u00b1 0.23\u00a0mm compared to other state-of-the-art methods. We used MRI images from 263 patients to demonstrate the efficiency of our proposed method.<\/jats:p>","DOI":"10.1093\/jcde\/qwab030","type":"journal-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T11:50:23Z","timestamp":1620301823000},"page":"1023-1036","source":"Crossref","is-referenced-by-count":30,"title":["Intervertebral disc instance segmentation using a multistage optimization mask-RCNN (MOM-RCNN)"],"prefix":"10.1093","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1946-0678","authenticated-orcid":false,"given":"Malinda","family":"Vania","sequence":"first","affiliation":[{"name":"Center for Healthcare Robotics, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea"},{"name":"Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Republic of Korea"}]},{"given":"Deukhee","family":"Lee","sequence":"additional","affiliation":[{"name":"Center for Healthcare Robotics, 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