Open AccessAutomatic segmentation of the femur and tibia bones from X-ray images based on pure dilated residual U-Net
Author(s) -
Weihao Shen,
Wenbo Xu,
Hongyang Zhang,
ZhangHua Sun,
Jianxiong Ma,
Xinlong Ma,
S. Kevin Zhou,
Shuxiang Guo,
Yuanquan Wang
Publication year - 2021
Publication title -
inverse problems and imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.755
H-Index - 40
eISSN - 1930-8345
pISSN - 1930-8337
DOI - 10.3934/ipi.2020057
Subject(s) - residual , segmentation , tibia , femur , convolution (computer science) , computer science , artificial intelligence , noise (video) , image segmentation , computer vision , medicine , algorithm , anatomy , image (mathematics) , surgery , artificial neural network
X-ray images of the lower limb bone are the most commonly used imaging modality for clinical studies, and segmentation of the femur and tibia in an X-ray image is helpful for many medical studies such as diagnosis, surgery and treatment. In this paper, we propose a new approach based on pure dilated residual U-Net for the segmentation of the femur and tibia bones. The proposed approach employs dilated convolution completely to increase the receptive field, in this way, we can make full use of the advantages of dilated convolution. We conducted experiments and evaluations on datasets provided by Tianjin hospital. Comparison with the classical U-net and FusionNet, our method has fewer parameters, higher accuracy, and converges more rapidly, which means the high performance of the proposed method.