Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation

Asaturyan, H., Gligorievski, A. and Villarini, B. 2019. Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation. Computerized Medical Imaging and Graphics. 75, pp. 1-13. doi:10.1016/j.compmedimag.2019.04.004

TitleMorphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation
TypeJournal article
AuthorsAsaturyan, H., Gligorievski, A. and Villarini, B.
Abstract

Automatic pancreas segmentation in 3D radiological scans is a critical, yet challenging task. As a prerequisite for computer-aided diagnosis (CADx) systems, accurate pancreas segmentation could generate both quantitative and qualitative information towards establishing the severity of a condition, and thus provide additional guidance for therapy planning. Since the pancreas is an organ of high inter-patient anatomical variability, previous segmentation approaches report lower quantitative accuracy scores in comparison to abdominal organs such as the liver or kidneys. This paper presents a novel approach for automatic pancreas segmentation in magnetic resonance imaging (MRI) and computer tomography (CT) scans. This method exploits 3D segmentation that, when coupled with geometrical and morphological characteristics of abdominal tissue, classifies distinct contours in tight pixel-range proximity as “pancreas” or “non-pancreas”. There are three main stages to this approach: (1) identify a major pancreas region and apply contrast enhancement to differentiate between pancreatic and surrounding tissue; (2) perform 3D segmentation via continuous max-flow and min-cuts approach, structured forest edge detection, and a training dataset of annotated pancreata; (3) eliminate non-pancreatic contours from resultant segmentation via morphological operations on area, structure and connectivity between distinct contours. The proposed method is evaluated on a dataset containing 82 CT image volumes, achieving mean Dice Similarity coefficient (DSC) of 79.3 ± 4.4%. Two MRI datasets containing 216 and 132 image volumes are evaluated, achieving mean DSC 79.6 ± 5.7% and 81.6 ± 5.1% respectively. This approach is statistically stable, reflected by lower metrics in standard deviation in comparison to state-of-the-art approaches.

KeywordsAutomatic pancreas segmentationComputer-aided diagnosisContinuous max-flow and min-cutsContrast enhancementGeometrical characteristicsStructured forest
JournalComputerized Medical Imaging and Graphics
Journal citation75, pp. 1-13
ISSN0895-6111
Year2019
PublisherElsevier
Digital Object Identifier (DOI)doi:10.1016/j.compmedimag.2019.04.004
Web address (URL)http://www.sciencedirect.com/science/article/pii/S0895611118304701
Publication dates
Published16 May 2019
Published in printJul 2019
LicenseCC BY-NC-ND 4.0

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