Intervertebral disc instance segmentation using a multistage optimization mask-RCNN (MOM-RCNN). Issue 4 (18th June 2021)

• Record Type: Journal Article
• Authors: Vania, Malinda; Lee, Deukhee
• Is Part Of: Journal of computational design and engineering. Volume 8:Issue 4(2021)
• Page Start: 1023
• Page End: 1036
• Publication Date: 2021-06-18
• Subjects: magnetic resonance image -- intervertebral discs segmentation -- automatic instance segmentation -- regional convolutional neural network -- multistage optimization mask-RCNN -- multistage segmentation; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwab030
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: Lower back pain is one of the major global challenges in health problems. 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's 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 ± 0.067 mm in root mean square error to 0.095 ± 0.026 mm, Hausdorff distance from 12.313 ± 3.015 to 5.155 ± 1.561 mm, and average symmetric surface distance from 1.944 ± 0.850 to 0.49 ± 0.23 mm compared to other state-of-the-art methods. We used MRI images from 263 patients to demonstrate the efficiency of our proposed method. Graphical Abstract: