Vertebrae localization and spine segmentation on radiographic images for feature‐based curvature classification for scoliosis. (29th August 2022)

• Record Type: Journal Article
• Authors: Fatima, Joddat; Mohsan, Mashood; Jameel, Amina; Akram, Muhammad Usman; Muzaffar Syed, Adeel
• Is Part Of: Concurrency and computation. Volume 34:Number 26(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-29
• Subjects: convolutional neural networks -- mask RCNN -- spinal cord -- vertebrae segmentation -- YOLO; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7300
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Spinal cord is the one of the most important organs in the central nervous system (CNS). It acts as the main processing hub which serves as the main passage line for information transfer from brain to the rest of the body. It supports the whole skeleton structure along with mobility, bending, turning, twisting and so forth. Several factors may result in the deformity of spine such as a major injury, fracture or a defect by birth. In this research, we have discussed two modules: one is for vertebrae localization and spine segmentation and the second one is for analysis of spine dis‐proportionality. A recent approach of YOLOv5 is used for the localization of vertebrae in combination with Mask RCNN for segmentation of spinal column. The combined results from both these modules are used for feature extraction which supports our classification‐based shape analysis module. The AASCE 2019 challenge dataset is used to evaluate the experimental results and the value of mAP achieved is 0.94 at 0.5 IOU threshold of YOLOv5 model. The proposed technique with novel feature set achieved an average classification accuracy of 94.69%.