Abstract

Info

Title:: Progress on convolutional neural network in medical image segmentation

Author(s):: XU Hang1; SUI Li1; ZHANG Jingwen2; ZHAO Yanfu1; LI Yueru1; 1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2. Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China

Keywords:: convolutional neural network; medical image; image segmentation; deep learning; review

Abstract:: Convolutional neural network (CNN) is regarded as the state-of-the-art algorithm in computer vision and pattern recognition. CNN which is excellent for spatial recognition can extract hierarchical features from images. The number of parameters is greatly reduced by sharing kernels, thus improving the network performance and keeping the number of total parameters in a reasonable and computable range. To some extent, some CNN algorithms based on unsupervised learning can perform semantic segmentation without prior knowledge, releasing the burden of manual works. Herein the research progresses of CNN in medical image segmentation as well as the techniques and outcomes of CNN are reviewed. Focusing on the topic of using deep learning with CNN as core to realize medical image segmentation, the great potentials of CNN in supervised learning, semi-supervised learning and unsupervised learning are introduced. By analyzing and comparing advantages and shortages of existing methods, the prospects of CNN in medical imaging are discussed.