A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain

Abstract

Medical Image Segmentation is the process of automatic or semi-automatic detection of boundaries within a 2D or 3D image in Internet-of-Medical-Things (IoMT) domain. The main difficulty of medical image segmentation is the high variability in medical images. For example, CT images contain a large amount of noise, and complex boundaries. In this paper, we propose an adaptive fully dense(AFD) neural network for CT image segmentation. By adding the horizontal connections in UNet structure, it can extract various features from all layers adaptively. And it use ensemble training for the output to extract more edge information in the multiple rounds training. We have validated our method on two data sets, a natural scene image data set and a liver cancer CT image data set. The experimental results demonstrate that it performs better than state-of-the-art segmentation methods. And our method yields superior segmentation results for CT images with complex boundaries.