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Dima, A.* ; Paetzold, J.C. ; Jungmann, F.* ; Lemke, T.* ; Raffler, P.* ; Kaissis, G.* ; Rueckert, D.* ; Braren, R.*

Segmentation of peripancreatic arteries in multispectral computed tomography imaging.

In: (12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, 27 September 2021, Virtual, Online). Berlin [u.a.]: Springer, 2021. 596-605 (Lect. Notes Comput. Sc. ; 12966 LNCS)
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Pancreatic ductal adenocarcinoma is an aggressive form of cancer with a poor prognosis, where the operability and hence chance of survival is strongly affected by the tumor infiltration of the arteries. In an effort to enable an automated analysis of the relationship between the local arteries and the tumor, we propose a method for segmenting the peripancreatic arteries in multispectral CT images in the arterial phase. A clinical dataset was collected, and we designed a fast semi-manual annotation procedure, which requires around 20 min of annotation time per case. Next, we trained a U-Net based model to perform binary segmentation of the peripancreatic arteries, where we obtained a near perfect segmentation with a Dice score of 95.05 % in our best performing model. Furthermore, we designed a clinical evaluation procedure for our models; performed by two radiologists, yielding a complete segmentation of 85.31 % of the clinically relevant arteries, thereby confirming the clinical relevance of our method.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Annotation ; Arterial Segmentation ; Pdac ; Vessels
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021
Conference Date 27 September 2021
Conference Location Virtual, Online
Quellenangaben Volume: 12966 LNCS, Issue: , Pages: 596-605 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute for Tissue Engineering and Regenerative Medicine (ITERM)