Recognition of necrotic regions in MRI images of chronic spinal cord injury based on superpixel. (January 2023)

• Record Type: Journal Article
• Authors: Bao, Xing-Xing; Zhao, Can; Bao, Shu-Sheng; Rao, Jia-Sheng; Yang, Zhao-Yang; Li, Xiao-Guang
• Is Part Of: Computer methods and programs in biomedicine. Volume 228(2023)
• Publication Date: 2023-01
• Subjects: Image recognition -- Chronic spinal cord injury -- Magnetic resonance imaging -- Necrotic region -- Superpixel; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107252
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The combination of CAD technology and MRI technology is used to recognize the necrotic region formed by the scar and cystic cavity in chronic SCI. Superpixel is generated in the multimodal fusion image of the spinal cord based on DT-CWT. The recognition method based on the combination of superpixel and SVM technology is insensitive to the shape and size of the spinal necrosis area. Abstract: Background and objective: The cystic cavity and its surrounding dense glial scar formed in chronic spinal cord injury (SCI) hinder the regeneration of nerve axons. Accurate location of the necrotic regions formed by the scar and the cavity is conducive to eliminate the re-growth obstacles and promote SCI treatment. This work aims to realize the accurate and automatic location of necrotic regions in the chronic SCI magnetic resonance imaging (MRI). Methods: In this study, a method based on superpixel is proposed to identify the necrotic regions of spinal cord in chronic SCI MRI. Superpixels were obtained by a simple linear iterative clustering algorithm, and feature sets were constructed from intensity statistical features, gray level co-occurrence matrix features, Gabor texture features, local binary pattern features and superpixel areas. Subsequently, the recognition effects of support vector machine (SVM) and random forest (RF) classification model on necrotic regions were compared from accuracy (ACC), positive predictive value (PPV), sensitivity (SE), specificity (SP), Dice coefficient and algorithm running time. Results: The method is evaluated on T1- and T2-weighted MRI spinal cord images of 24 adult female Wistar rats. And an automatic recognition method for spinal cord necrosis regions was established based on the SVM classification model finally. The recognition results were 1.00±0.00 (ACC), 0.89±0.09 (PPV), 0.88±0.12 (SE), 1.00±0.00 (SP) and 0.88±0.07 (Dice), respectively. Conclusions: The proposed method can accurately and noninvasively identify the necrotic regions in MRI, which is helpful for the pre-intervention assessment and post-intervention evaluation of chronic SCI research and treatments, and promoting the clinical transformation of chronic SCI research.