Pancreas Segmentation-Derived Biomarkers: Volume and Shape Metrics in the UK Biobank Imaging Study

Abstract

Quantitative imaging biomarkers derived from magnetic resonance imaging of the pancreas could reveal changes in pancreas organ volume and shape manifest in chronic disease. Recent developments in machine learning facilitate pancreas segmentation and volume extraction. Machine learning methods could also help in designing a data-driven approach to pancreas shape characterization. We present an automated pipeline for pancreas volume and shape characterization. We start off with deep learning-based segmentation; we show the impact of choice of loss function in pancreas segmentation by comparing a 3D U-Net model trained using soft Dice over cross-entropy loss. Then, a diffeomorphic algorithm for group-wise registration as well as manifold learning are used to extract prominent shape features from the segmentation masks. The technique shows potential in a subset (N = 3,909) of the UK Biobank imaging sub-study for (1) automated quality control, e.g. suboptimal pancreas coverage acquisitions; and (2) determining abnormal pancreas morphology, that might reflect different patterns of fat infiltration. To our knowledge, this work is the first to attempt learning pancreas shape features.