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17 October 2022 Iodine-enhanced liver vessel segmentation in photon counting detector-based computed tomography using deep learning
Sumin Baek, Okkyun Lee, Donghye Ye
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Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 1230432 (2022) https://doi.org/10.1117/12.2646541
Event: Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), 2022, Baltimore, United States
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.
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Sumin Baek, Okkyun Lee, and Donghye Ye "Iodine-enhanced liver vessel segmentation in photon counting detector-based computed tomography using deep learning", Proc. SPIE 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography, 1230432 (17 October 2022); https://doi.org/10.1117/12.2646541
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KEYWORDS
Liver
Image segmentation
Iodine
X-ray computed tomography
Computed tomography
3D image processing
Photon counting
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