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2017 | Supplement | Buchkapitel

3D U-net with Multi-level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images

verfasst von : Guodong Zeng, Xin Yang, Jing Li, Lequan Yu, Pheng-Ann Heng, Guoyan Zheng

Erschienen in: Machine Learning in Medical Imaging

Verlag: Springer International Publishing

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Abstract

This paper 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.

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https://github.com/zengguodong/FemurSegmentation3DFCN.

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Titel: 3D U-net with Multi-level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images
verfasst von: Guodong Zeng
Xin Yang
Jing Li
Lequan Yu
Pheng-Ann Heng
Guoyan Zheng
Verlag: Springer International Publishing
Buch: Machine Learning in Medical Imaging
Print ISBN: 978-3-319-67388-2

Electronic ISBN: 978-3-319-67389-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-67389-9_32

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