Segmentation of Peripancreatic Arteries in Multispectral Computed Tomography Imaging

Alina Dima, Johannes C. Paetzold, Friederike Jungmann, Tristan Lemke, Philipp Raffler, Georgios Kaissis, Daniel Rueckert, Rickmer Braren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsChunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages596-605
Number of pages10
ISBN (Print)9783030875886
DOIs
StatePublished - 2021
Event12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sep 202127 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12966 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/2127/09/21

Keywords

  • Annotation
  • Arterial segmentation
  • PDAC
  • Vessels

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