Automatic Segmentation of Anatomical Areas in X-ray Images Based on Fully Convolutional Networks.
10.3969/j.issn.1671-7104.2019.03.004
- Author:
Lei GUO
1
;
Yujun WANG
1
;
Hongwei HE
1
;
Changyuan WANG
1
;
Lu LIU
1
;
Xiuyun YANG
1
Author Information
1. Modern Educational Technology Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, 271016.
- Publication Type:Journal Article
- Keywords:
X-ray image;
anatomical area;
fully convolutional networks;
image feature;
medical image segmentation
- MeSH:
Algorithms;
Image Processing, Computer-Assisted;
Neural Networks (Computer);
X-Rays
- From:
Chinese Journal of Medical Instrumentation
2019;43(3):170-172
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:Medical image segmentation is a key step in medical image processing. An architecture of fully convolutional networks was proposed to realize automatic segmentation of anatomical areas in X-ray images.
METHODS:Enlightened by the advantages of convolutional neural networks on features extraction, fully convolutional networks consisting of 9 layers were designed to segment medical images. The networks used convolution kernels of various sizes to extract multi-dimensional image features in the images, meanwhile, eliminated pooling layers to avoid the loss of image details during downsampling procedures.
RESULTS:The experiment was conducted in accordance with the specific scene of X-ray images segmentation. Compared with traditional segmentation methods, this approach achieved more accurate segmentation of anatomical areas.
CONCLUSIONS:Fully convolutional networks can extract representative and multidimensional features of medical images, avoid the loss of image details during downsampling procedures, and complete automatic segmentation of anatomical areas accurately in X-ray images.
