TY - GEN
T1 - Segmentation of Peripancreatic Arteries in Multispectral Computed Tomography Imaging
AU - Dima, Alina
AU - Paetzold, Johannes C.
AU - Jungmann, Friederike
AU - Lemke, Tristan
AU - Raffler, Philipp
AU - Kaissis, Georgios
AU - Rueckert, Daniel
AU - Braren, Rickmer
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Annotation
KW - Arterial segmentation
KW - PDAC
KW - Vessels
UR - http://www.scopus.com/inward/record.url?scp=85116494976&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87589-3_61
DO - 10.1007/978-3-030-87589-3_61
M3 - Conference contribution
AN - SCOPUS:85116494976
SN - 9783030875886
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 596
EP - 605
BT - Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Lian, Chunfeng
A2 - Cao, Xiaohuan
A2 - Rekik, Islem
A2 - Xu, Xuanang
A2 - Yan, Pingkun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th 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
Y2 - 27 September 2021 through 27 September 2021
ER -
