Simultaneous Recognition and Segmentation of Multiple Anatomical Structures on CT Images by Using Deep Learning Approach

Abstract

This paper introduces research works that apply deep learning approaches based on ConvNet to solve automatic multi-organ segmentations on CT images that cover a wide range of human body. In particular, we describe our recent research work as an example to show multiple-organ segmentation methods on CT images by using ConvNets. We discuss strength and weakness of the ConvNet that is majorly used for 2D image processing and its extension for 3D images with the latest research progresses. Finally, we compare the deep learning approaches to the conventional approach that is designed by the processing procedures based on human experience and shows an advantage and potential possibility of ConvNets to address the issue of automatic multi-organ segmentations on CT images covering a wide range of human body.