Deep Learning-Based Automatic Segmentation of the Proximal Femur from MR Images

Guodong Zeng; Guoyan Zheng

doi:10.1007/978-981-13-1396-7_6

Deep Learning-Based Automatic Segmentation of the Proximal Femur from MR Images

Adv Exp Med Biol. 2018:1093:73-79. doi: 10.1007/978-981-13-1396-7_6.

Authors

Guodong Zeng¹, Guoyan Zheng²

Affiliations

¹ Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland.
² Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland. guoyan.zheng@istb.unibe.ch.

PMID: 30306473
DOI: 10.1007/978-981-13-1396-7_6

Abstract

This chapter addresses the problem of segmentation of proximal femur in 3D MR images. We propose a deeply supervised 3D U-net-like fully convolutional network for segmentation of proximal femur in 3D MR images. After training, our network can directly map a whole volumetric data to its volume-wise labels. Inspired by previous work, multi-level deep supervision is designed to alleviate the potential gradient vanishing problem during training. It is also used together with partial transfer learning to boost the training efficiency when only small set of labeled training data are available. The present method was validated on 20 3D MR images of femoroacetabular impingement patients. The experimental results demonstrate the efficacy of the present method.

Keywords: Deep learning; Deep supervision; Femoroacetabular impingement (FAI); Fully Convolutional Network (FCN); MRI; Proximal femur; Segmentation.

MeSH terms

Deep Learning*
Femoracetabular Impingement / diagnostic imaging
Femur / diagnostic imaging*
Humans
Imaging, Three-Dimensional*
Magnetic Resonance Imaging*