Automated Pancreatic Segmentation and Quantitative Calculation Based on nnUnet

Motivation: Pancreatic diseases often exhibit spatial non-uniformity. Achieving automated segmentation of different pancreatic regions and conducting quantitative calculations of volume and fat content can effectively assist physicians in diagnosis and treatment. Goal(s): Developing a segmentation network to achieve the automatic segmentation of the pancreas and to perform quantitative calculations of volume and fat content in diffrent regions. Approach: Sample acquisition was performed using Dixon sequences, and training was conducted using an improved nnUnet network. Additionally, an automated pancreatic segmentation and quantitative calculation method was developed. Results: With a training dataset consisting of 800 cases, the network achieved a segmentation Dice coefficient of 0.92. Impact: To save professional physician annotation time for early detection and diagnosis of pancreatic diseases, as well as for quantifying changes before and after pancreatic treatments, and to assist in clinical drug therapy.