Quantification of 2D shape variation of abnormal proximal femurs over normal proximal femurs in bilateral hip MRI: A computerized analysis for Legg-Calve-Perthes disease

A novel approach for computerized quantitative image analysis of proximal femur bone shape deformities based on the hip joint symmetry

As a result of most of the bone disorders seen in hip joints, shape deformities occur in the structural form of the hip joint components. Image-based quantitative analysis and assessment of these deformities in bone shapes are very important for the evaluation, treatment, and prognosis of the various hip joint bone disorders. In this article, a novel approach for the image-based computerized quantitative analysis of proximal femur shape deformities is presented. In the proposed approach, shape deformities of the pathological proximal femurs were quantified over the contralateral healthy proximal femur shape structure of the same patient in 2D by taking the hip joint symmetry property of human anatomy into consideration. It is based on the idea that if the right and left proximal femurs in bilateral hip joints are highly symmetrical and also if one of the proximal femurs is healthy and the contralateral one is pathological, the non-overlapping bone shape regions can represent the deformities in pathological proximal femurs when both proximal femurs are registered to overlap each other. In the methodological process of the proposed study, a set of image preprocessing operations was primarily performed on the raw magnetic resonance imaging (MRI) data. Then, the segmented proximal femurs in bilateral hip joint images were automatically aligned with the Iterative Closest Point (ICP) rigid registration method. Following the registration, a set of image postprocessing operations was performed on the images of proximal femurs aligned. In the quantification phase, the bone shape deformities in pathological proximal femurs were quantified simply in terms of the mismatching area in 2D by measuring a shape variation index representing the total bone shape deformity ratio. To evaluate the proposed quantitative shape analysis approach, bilateral hip joints in a total of 13 coronal MRI sections of 13 patients with Legg-Calve-Perthes disease (LCPD) were used. Experimental studies have shown that the proposed approach has quite promising results in the quantitative representation of the pathological proximal femur shape deformities. Furthermore, consistent results have been observed for the Waldenström classification stages of the disease. The shape deformity ratios in pathological proximal femurs were quantified as 9.44% (±1.40), 18.38% (±6.30), 24.73% (±12.42), and 27.66% (±10.41), respectively for the Initial, Fragmentation, Reossification, and Remodelling stages of LCPD with the quantification error rates of 0.29% (±0.16), 0.58% (±0.71), 1.12% (±0.82), and 0.80% (±0.98). Additionally, a mean error rate of 0.65% (±0.68) was observed for the quantified shape deformity ratios of all samples.

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

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.