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

•This paper proposes a 2D/3D approach for pancreas segmentation in multimodality radiological scans (image volumes).•A novel post-processing stage, comprising of three levels, improves final tissue classification through analysis of distinct contours, and every increasing level progressively targets surrounding tissue that is located in closer proximity to the pancreas.•The proposed approach produces detailed boundary preservation and greater consistency in spatial smoothness, and tissue classification among successive slices in the image volume.•Segmentation accuracy results show robust statistical stability across multiple modalities (i.e., CT and MRI) and across datasets that were obtained using different scanner imaging protocols.•The proposed approach can be applied to other abdominal MRI and CT sequences and also, generalisable to other organ or muscular tissue segmentation tasks. 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 2