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Model-Based Pancreas Segmentation in Portal Venous Phase Contrast-Enhanced CT Images

Hammon, Matthias and Cavallaro, Alexander and Erdt, Marius and Dankerl, Peter and Kirschner, Matthias and Drechsler, Klaus and Wesarg, Stefan and Uder, Michael and Janka, Rolf (2014):
Model-Based Pancreas Segmentation in Portal Venous Phase Contrast-Enhanced CT Images.
In: Journal of Digital Imaging, pp. 1082-1090Firstublishedonline08March2013, 26, (6), DOI: 10.1007/s10278-013-9586-7,
[Article]

Abstract

This study aims to automatically detect and segment the pancreas in portal venous phase contrast-enhanced computed tomography (CT) images. The institutional review board of the University of Erlangen-Nuremberg approved this study and waived the need for informed consent. Discriminative learning is used to build a pancreas tissue classifier incorporating spatial relationships between the pancreas and surrounding organs and vessels. Furthermore, discrete cosine and wavelet transforms are used to build texture features to describe local tissue appearance. Classification is used to guide a constrained statistical shape model to fit the data. The algorithm to detect and segment the pancreas was evaluated on 40 consecutive CT data that were acquired in the portal venous contrast agent phase. Manual segmentation of the pancreas was carried out by experienced radiologists and served as reference standard. Threefold cross validation was performed. The algorithm-based detection and segmentation yielded an average surface distance of 1.7 mm and an average overlap of 61.2 compared with the reference standard. The overall runtime of the system was 20.4 min. The presented novel approach enables automatic pancreas segmentation in portal venous phase contrast-enhanced CT images which are included in almost every clinical routine abdominal CT examination. Reliable pancreatic segmentation is crucial for computer-aided detection systems and an organ-specific decision support.

Item Type: Article
Erschienen: 2014
Creators: Hammon, Matthias and Cavallaro, Alexander and Erdt, Marius and Dankerl, Peter and Kirschner, Matthias and Drechsler, Klaus and Wesarg, Stefan and Uder, Michael and Janka, Rolf
Title: Model-Based Pancreas Segmentation in Portal Venous Phase Contrast-Enhanced CT Images
Language: English
Abstract:

This study aims to automatically detect and segment the pancreas in portal venous phase contrast-enhanced computed tomography (CT) images. The institutional review board of the University of Erlangen-Nuremberg approved this study and waived the need for informed consent. Discriminative learning is used to build a pancreas tissue classifier incorporating spatial relationships between the pancreas and surrounding organs and vessels. Furthermore, discrete cosine and wavelet transforms are used to build texture features to describe local tissue appearance. Classification is used to guide a constrained statistical shape model to fit the data. The algorithm to detect and segment the pancreas was evaluated on 40 consecutive CT data that were acquired in the portal venous contrast agent phase. Manual segmentation of the pancreas was carried out by experienced radiologists and served as reference standard. Threefold cross validation was performed. The algorithm-based detection and segmentation yielded an average surface distance of 1.7 mm and an average overlap of 61.2 compared with the reference standard. The overall runtime of the system was 20.4 min. The presented novel approach enables automatic pancreas segmentation in portal venous phase contrast-enhanced CT images which are included in almost every clinical routine abdominal CT examination. Reliable pancreatic segmentation is crucial for computer-aided detection systems and an organ-specific decision support.

Journal or Publication Title: Journal of Digital Imaging
Volume: 26
Number: 6
Uncontrolled Keywords: Forschungsgruppe Medical Computing (MECO), Business Field: Visual decision support, Research Area: Confluence of graphics and vision, Computed tomography (CT), Segmentation, Detection, Machine learning
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1007/s10278-013-9586-7
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