Deep volumetric shape learning for semantic segmentation of the Hip joint from 3D MR images

Abstract

This paper addresses the problem of segmentation of the hip joint including both the acetabulum and the proximal femur in three-dimensional magnetic resonance images. We propose a fully convolutional volumetric auto encoder that learns a volumetric representation from manual segmentation in order to regularize the segmentation results obtained from a fully convolutional network. We further introduce a super resolution network to improve the segmentation accuracy. Comprehensive results obtained from 24 patient data demonstrated the effectiveness of the proposed framework.