Author:
Chen Lei,Song Hong,Wang Chi,Cui Yutao,Yang Jian,Hu Xiaohua,Zhang Le
Abstract
Abstract
Background
Malignant liver tumor is one of the main causes of human death. In order to help physician better diagnose and make personalized treatment schemes, in clinical practice, it is often necessary to segment and visualize the liver tumor from abdominal computed tomography images. Due to the large number of slices in computed tomography sequence, developing an automatic and reliable segmentation method is very favored by physicians. However, because of the noise existed in the scan sequence and the similar pixel intensity of liver tumors with their surrounding tissues, besides, the size, position and shape of tumors also vary from one patient to another, automatic liver tumor segmentation is still a difficult task.
Results
We perform the proposed algorithm to the Liver Tumor Segmentation Challenge dataset and evaluate the segmentation results. Experimental results reveal that the proposed method achieved an average Dice score of 68.4% for tumor segmentation by using the designed network, and ASD, MSD, VOE and RVD improved from 27.8 to 21, 147 to 124, 0.52 to 0.46 and 0.69 to 0.73, respectively after performing adversarial training strategy, which proved the effectiveness of the proposed method.
Conclusions
The testing results show that the proposed method achieves improved performance, which corroborated the adversarial training based strategy can achieve more accurate and robustness results on liver tumor segmentation task.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Reference33 articles.
1. Zheng Z, Zhang X, Xu H, Liang W, Zheng S, Shi Y. A unified level set framework combining hybrid algorithms for liver and liver tumor segmentation in ct images. BioMed Res Int. 2018; 2018. https://doi.org/10.1155/2018/3815346.
2. Yan J, Schwartz LH, Zhao B. Semiautomatic segmentation of liver metastases on volumetric ct images. Med Phys. 2015; 42(11):6283–93.
3. Massoptier L, Casciaro S. A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from ct scans. Eur Radiol. 2008; 18(8):1658.
4. Wong D, Liu J, Fengshou Y, Tian Q, Xiong W, Zhou J, Qi Y, Han T, Venkatesh S, Wang S-c. A semi-automated method for liver tumor segmentation based on 2d region growing with knowledge-based constraints. In: MICCAI Workshop, vol. 41. Berlin: Springer-Verlag Berlin Heidelberg: 2008. p. 159.
5. Yim PJ, Foran DJ. Volumetry of hepatic metastases in computed tomography using the watershed and active contour algorithms. In: 16th IEEE Symposium Computer-Based Medical Systems, 2003. Proceedings. IEEE: 2003. p. 329–35. https://doi.org/10.1109/cbms.2003.1212810.
Cited by
35 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献