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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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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Annotation ; Arterial Segmentation ; Pdac ; Vessels
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021
Konferzenzdatum 27 September 2021
Konferenzort Virtual, Online
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 12966 LNCS, Heft: , Seiten: 596-605 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505800-001
DOI 10.1007/978-3-030-87589-3_61
Scopus ID 85116494976
Erfassungsdatum 2021-11-30
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