Semantic segmentation of the multiform proximal femur and femoral head bones with the deep convolutional neural networks in low quality MRI sections acquired in different MRI protocols. (April 2020)

• Record Type: Journal Article
• Authors: Memiş, Abbas; Varlı, Songül; Bilgili, Fuat
• Is Part Of: Computerized medical imaging and graphics. Volume 81(2020)
• Publication Date: 2020-04
• Subjects: MR image segmentation -- Legg–Calve–Perthes disease -- Femoral head segmentation -- Proximal femur segmentation -- Convolutional neural networks; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2020.101715
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: Femoral head and proximal femur bones in MRI were segmented semantically with CNNs. –Multiform bones (both spheric and aspheric) were analyzed in segmentation process. A total of 194 MRI sections in 33 MRI sequences of 13 LCPD patients were evaluated. Low quality MRI data acquired in different protocols with 1.5 T scanners was used. An average success rate about 90% was observed in proposed segmentation study. Abstract: Medical image segmentation is one of the most crucial issues in medical image processing and analysis. In general, segmentation of the various structures in medical images is performed for the further image analyzes such as quantification, assessment, diagnosis, prognosis and classification. In this paper, a research study for the 2D semantic segmentation of the multiform, both spheric and aspheric, femoral head and proximal femur bones in magnetic resonance imaging (MRI) sections of the patients with Legg–Calve–Perthes disease (LCPD) with the deep convolutional neural networks (CNNs) is presented. In the scope of the proposed study, bilateral hip MRI sections acquired in coronal plane were used. The main characteristic of the MRI sections that were used is to be low quality images which were obtained in different MRI protocols by using 3 different MRI scanners with 1.5 T imaging capability. In performance evaluations, promising segmentation results were achieved with deep CNNs in low quality MRI sections acquired in different MRI protocols. A success rate about 90% was observed in semantic segmentation of the multiform femoral head and proximal femur bones in a total of 194 MRI sections obtained from 33 MRI sequences of 13 patients with deep CNNs.