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.
Subject(s)
Algorithms , Image Processing, Computer-Assisted , Neural Networks, Computer , X-RaysABSTRACT
With the development of image-guided surgery and radiotherapy, the demand for medical image registration is stronger and the challenge is greater. In recent years, deep learning, especially deep convolution neural networks, has made excellent achievements in medical image processing, and its research in registration has developed rapidly. In this paper, the research progress of medical image registration based on deep learning at home and abroad is reviewed according to the category of technical methods, which include similarity measurement with an iterative optimization strategy, direct estimation of transform parameters, etc. Then, the challenge of deep learning in medical image registration is analyzed, and the possible solutions and open research are proposed.