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.

A new scheme for automatic 2D detection of spheric and aspheric femoral heads: A case study on coronal MR images of bilateral hip joints of patients with Legg-Calve-Perthes disease.

BACKGROUND AND OBJECTIVE: In orthopaedics, hip joint and circular structure of the femoral head can be distorted by a wide variety of hip joint disorders. Hence, automatic detection and segmentation of the femoral head is an important issue in the studies of computerized hip joint segmentation, quantification and assessment. In the proposed study, we need to detect the center coordinates and radius of spheric and aspheric femoral heads automatically in bilateral hip magnetic resonance (MR) images using a new scheme. METHODS: This paper presents a new two-level scheme based on the Circular Hough Transform (CHT) and Integro-differential Operator (IDO) to detect the spheric and aspheric femoral heads in MR images in 2D. Initially, MR slices are divided vertically into two equal halves to automatically separate the hip joints and Canny's edge detection method is performed on each of the halves to obtain edge images. Then, healthy and pathological femoral heads are detected by performing the CHT over edge images in the first stage of proposed scheme. In the second stage, femoral head circle detected with CHT is fine tuned by performing Daugman's IDO. RESULTS: Performance evaluations of the proposed femoral head detection scheme were carried out on healthy and pathological hip joints in 24 coronal MR image sections belong to 13 patients with Legg-Calve-Perthes Disease (LCPD). In performance evaluations on 24 healthy and 24 pathological hip joints, Root Mean Square Error (RMSE) and Dice Similarity Coefficient (DSC) values were measured for automatically detected femoral heads. We observed 1.96 mm. (std. 1.21 mm.) mean RMSE for center coordinates, 1.45 mm. (std. 1.39 mm) mean RMSE for radii, 0.8978 (std. 0.0733) mean DSC on healthy femoral heads and 3.56 mm. (std. 3.19 mm.) mean RMSE for center coordinates, 1.56 mm. (std. 1.33 mm.) mean RMSE for radii, 0.8529 (std. 0.0927) mean DSC on pathological femoral heads. CONCLUSIONS: Proposed femoral head detection scheme has promising results for the detection and the segmentation of the spheric and aspheric femoral heads and also has a potential to be used in detection of the other anatomical structures having a circular shape.