Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Baek, Sumin | - |
dc.contributor.author | Lee, Okkyun | - |
dc.contributor.author | Ye, Donghye | - |
dc.date.accessioned | 2023-12-26T18:13:20Z | - |
dc.date.available | 2023-12-26T18:13:20Z | - |
dc.date.created | 2022-12-30 | - |
dc.date.issued | 2022-06-15 | - |
dc.identifier.isbn | 9781510656697 | - |
dc.identifier.issn | 0277-786X | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/46834 | - |
dc.description.abstract | Liver vessel segmentation is important in diagnosing and treating liver diseases. Iodine-based contrast agents are typically used to improve liver vessel segmentation by enhancing vascular structure contrast. However, conventional computed tomography (CT) is still limited with low contrast due to energy-integrating detectors. Photon counting detector-based computed tomography (PCD-CT) shows the high vascular structure contrast in CT images using multi-energy information, thereby allowing accurate liver vessel segmentation. In this paper, we propose a deep learning-based liver vessel segmentation method which takes advantages of the multi-energy information from PCD-CT. We develop a 3D UNet to segment vascular structures within the liver from 4 multi-energy bin images which separates iodine contrast agents. The experimental results on simulated abdominal phantom dataset demonstrated that our proposed method for the PCD-CT outperformed the standard deep learning segmentation method with conventional CT in terms of dice overlap score and 3D vascular structure visualization. © 2022 SPIE. | - |
dc.language | English | - |
dc.publisher | SPIE | - |
dc.title | Iodine-enhanced Liver Vessel Segmentation in Photon Counting Detector-based Computed Tomography using Deep Learning | - |
dc.type | Conference Paper | - |
dc.identifier.doi | 10.1117/12.2646541 | - |
dc.identifier.scopusid | 2-s2.0-85141770955 | - |
dc.identifier.bibliographicCitation | 7th International Conference on Image Formation in X-Ray Computed Tomography | - |
dc.identifier.url | https://ct-meeting.org/wp-content/uploads/2022/06/CT_Meeting_Proceedings.pdf | - |
dc.citation.conferencePlace | US | - |
dc.citation.conferencePlace | Baltimore | - |
dc.citation.title | 7th International Conference on Image Formation in X-Ray Computed Tomography | - |
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