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Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Liver tumor segmentation in CT volumes using an adversarial densely connected network
BMC bioinformatics, v 20(Suppl 16), pp 587-587
02 Dec 2019
PMID: 31787071
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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.
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.
Metrics
Details
- Title
- Liver tumor segmentation in CT volumes using an adversarial densely connected network
- Creators
- Lei Chen - Beijing Institute of TechnologyHong Song - Beijing Institute of TechnologyChi Wang - Beijing Institute of TechnologyYutao Cui - Beijing Institute of TechnologyJian Yang - Beijing Institute of TechnologyXiaohua Hu - Drexel UniversityLe Zhang - College of Computer Science, Sichuan University, Chengdu, China
- Publication Details
- BMC bioinformatics, v 20(Suppl 16), pp 587-587
- Publisher
- Springer BMC
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000499935900013
- Scopus ID
- 2-s2.0-85075872997
- Other Identifier
- 991019167460904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Mathematical & Computational Biology