Femoral Head Segmentation with Convolutional Neural Networks in MR Imaging Slices of the Patients with Legg-Calve-Perthes Disease

Abstract

In this paper, a study on semantic segmentation of the spheric (healthy) and aspheric (pathological) femoral heads in magnetic resonance (MR) slices with the deep Convolutional Neural Networks (CNNs) is presented. Femoral heads in bilateral hip MR slices were successfully segmented with U-NET is an encoder-decoder network based deep learning architecture. In the proposed study, bilateral hip MR slices which were acquired with different imaging protocols in coronal imaging plane of the patients diagnosed with Legg-Calve-Perthes (LCP) disease were used. In experimental studies, quite successful results have been achieved on a small amount of MR image data in segmentation of the healthy and pathological femoral heads. Performance tests evaluated on a total of 66 femoral head images in 33 MR slices of 13 LCP patients show that proposed study has a segmentation accuracy of approximately 89%.