Liver Tumor Segmentation and Subsequent Risk Prediction Based on Deeplabv3+

Abstract

As the largest glandular organ in the human body, liver has a large number of blood vessels and is connected with many important organs, such as spleen, pancreas and gallbladder, etc. The segmentation of liver and its lesions on medical images can help doctors accurately diagnose liver tumor and assess the probability of subsequent deterioration of the patient. Generally speaking, it is not only subjective but also wastes time if doctors rely on experience to manually analyze liver CT images. Therefore, it has been extensively studied in recent years. The segmentation of liver lesions is a kind of challenging task due to the low contrast ratio between the liver, lesions and nearby organs. To this end, we proposed to use the DeepLabV3+ semantic segmentation model based on the tensorflow architecture to segment the CT image of liver and locate the lesion positions. It combined deep convolutional neural networks (DCNNs) and probabilistic graphical model (DenseCRFs) and has been proven to have very good performance in a variety of computer vision tasks.