Morphology-guided deep learning framework for segmentation of pancreas in computed tomography images
Touseef Ahmad Qureshi, Cody Lynch, Linda Azab, Yibin Xie, Srinavas Gaddam, Stepehen Jacob Pandol, Debiao Li
Abstract
Purpose: Accurate segmentation of the pancreas using abdominal computed tomography (CT) scans is a prerequisite for a computer-aided diagnosis system to detect pathologies and perform quantitative assessment of pancreatic disorders. Manual outlining of the pancreas is tedious, time-consuming, and prone to subjective errors, and thus clearly not a viable solution for large datasets.
Topics & Concepts
SegmentationArtificial intelligenceMedicineConvolutional neural networkPancreasDeep learningComputed tomographyComputer visionImage segmentationRadiologyPattern recognition (psychology)Sørensen–Dice coefficientComputer scienceArtificial neural networkMedical imagingRegion of interestTomographyArtificial pancreasAbdominal computed tomographyScale-space segmentationCOVID-19 diagnosis using AIAdvanced Neural Network ApplicationsBrain Tumor Detection and Classification