Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT
Liver segmentation from CT is regarded as a prerequisite for computer-assisted clinical applications. However, automatic liver segmentation technology still faces challenges due to the variable shapes and low contrast. In this paper, a patient-specific probabilistic atlas (PA)-based method combing m...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-10-01
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| Series: | Computer Assisted Surgery |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24699322.2019.1649076 |
| _version_ | 1818776735348424704 |
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| author | Jinke Wang Hongliang Zu Haoyan Guo Rongrong Bi Yuanzhi Cheng Shinichi Tamura |
| author_facet | Jinke Wang Hongliang Zu Haoyan Guo Rongrong Bi Yuanzhi Cheng Shinichi Tamura |
| author_sort | Jinke Wang |
| collection | DOAJ |
| description | Liver segmentation from CT is regarded as a prerequisite for computer-assisted clinical applications. However, automatic liver segmentation technology still faces challenges due to the variable shapes and low contrast. In this paper, a patient-specific probabilistic atlas (PA)-based method combing modified distance regularized level set for liver segmentation is proposed. Firstly, the similarities between training atlases and testing patient image are calculated, resulting in a series of weighted atlas, which are used to generate the patient-specific PA. Then, a most likely liver region (MLLR) can be determined based on the patient-specific PA. Finally, the refinement is performed by the modified distance regularized level set model, which takes advantage of both edge and region information as balloon force. We evaluated our proposed scheme based on 35 public datasets, and experimental result shows that the proposed method can be deployed for robust and precise liver segmentation, to replace the tedious and time-consuming manual method. |
| first_indexed | 2024-12-18T11:17:39Z |
| format | Article |
| id | doaj.art-20b34e34fe394935a8c028f9a32e19cc |
| institution | Directory Open Access Journal |
| issn | 2469-9322 |
| language | English |
| last_indexed | 2024-12-18T11:17:39Z |
| publishDate | 2019-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Computer Assisted Surgery |
| spelling | doaj.art-20b34e34fe394935a8c028f9a32e19cc2022-12-21T21:09:54ZengTaylor & Francis GroupComputer Assisted Surgery2469-93222019-10-01240202610.1080/24699322.2019.16490761649076Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CTJinke Wang0Hongliang Zu1Haoyan Guo2Rongrong Bi3Yuanzhi Cheng4Shinichi Tamura5Harbin University of Science and TechnologySchool of Computer Science and Technology, Harbin University of Science and TechnologySchool of Computer Science and Technology, Harbin Institute of TechnologyHarbin University of Science and TechnologySchool of Computer Science and Technology, Harbin Institute of TechnologyCenter for Advanced Medical Engineering and Informatics, Osaka UniversityLiver segmentation from CT is regarded as a prerequisite for computer-assisted clinical applications. However, automatic liver segmentation technology still faces challenges due to the variable shapes and low contrast. In this paper, a patient-specific probabilistic atlas (PA)-based method combing modified distance regularized level set for liver segmentation is proposed. Firstly, the similarities between training atlases and testing patient image are calculated, resulting in a series of weighted atlas, which are used to generate the patient-specific PA. Then, a most likely liver region (MLLR) can be determined based on the patient-specific PA. Finally, the refinement is performed by the modified distance regularized level set model, which takes advantage of both edge and region information as balloon force. We evaluated our proposed scheme based on 35 public datasets, and experimental result shows that the proposed method can be deployed for robust and precise liver segmentation, to replace the tedious and time-consuming manual method.http://dx.doi.org/10.1080/24699322.2019.1649076Probabilistic atlaspatient-specificlevel setliver segmentation |
| spellingShingle | Jinke Wang Hongliang Zu Haoyan Guo Rongrong Bi Yuanzhi Cheng Shinichi Tamura Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT Computer Assisted Surgery Probabilistic atlas patient-specific level set liver segmentation |
| title | Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT |
| title_full | Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT |
| title_fullStr | Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT |
| title_full_unstemmed | Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT |
| title_short | Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT |
| title_sort | patient specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in ct |
| topic | Probabilistic atlas patient-specific level set liver segmentation |
| url | http://dx.doi.org/10.1080/24699322.2019.1649076 |
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