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

Asaturyan, Hykoush and Gligorievski, Antonio and Villarini, Barbara (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. ISSN 08956111

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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.

Item Type: Article
Impact Factor Value: 2.435
Subjects: Medical and Health Sciences > Clinical medicine
Medical and Health Sciences > Health sciences
Medical and Health Sciences > Other medical sciences
Divisions: Faculty of Medical Science
Depositing User: Antonio Gligorievski
Date Deposited: 08 Feb 2021 11:07
Last Modified: 08 Feb 2021 11:07

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