Open AccessLiver tumor segmentation based on 3D convolutional neural network with dual scale
Author(s) -
Meng Lu,
Tian Yaoyu,
Bu Sihang
Publication year - 2020
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1002/acm2.12784
Subject(s) - hausdorff distance , segmentation , artificial intelligence , convolutional neural network , pattern recognition (psychology) , computer science , ground truth , feature (linguistics) , scale space segmentation , dice , image segmentation , computer vision , path (computing) , segmentation based object categorization , mathematics , statistics , philosophy , linguistics , programming language
Purpose Liver is one of the organs with a high incidence of tumors in the human body. Malignant liver tumors seriously threaten human life and health. The difficulties of liver tumor segmentation from computed tomography (CT) image are: (a) The contrast between the liver tumors and healthy tissues in CT images is low and the boundary is blurred; (b) The image of liver tumor is complex and diversified in size, shape, and location. Methods To solve the above problems, this paper focused on the human liver and liver tumor segmentation algorithm based on convolutional neural network (CNN), and specially designed a three‐dimensional dual path multiscale convolutional neural network (TDP‐CNN). To balance the performance of segmentation and requirement of computational resources, the dual path was used in the network, then the feature maps from both paths were fused at the end of the paths. To refine the segmentation results, we used conditional random fields (CRF) to eliminate the false segmentation points in the segmentation results to improve the accuracy. Results In the experiment, we used the public dataset liver tumor segmentation (LiTS) to analyze the segmentation results qualitatively and quantitatively. Ground truth segmentation of liver and liver tumor was manually labeled by an experienced radiologist. Quantitative metrics were Dice, Hausdorff distance, and average distance. For the segmentation results of liver tumor, Dice was 0.689, Hausdorff distance was 7.69, and the average distance was 1.07; for the segmentation results of the liver, Dice was 0.965, Hausdorff distance was 29.162, and the average distance was 0.197. Compared with other liver and liver tumor segmentation algorithms in Medical Image Computing and Intervention (MICCAI) 2017 competition, our method of liver segmentation ranked first, and liver tumor segmentation ranked second. Conclusions The experimental results showed that the proposed algorithm had good performance in both liver and liver tumor segmentation.