Identifying Periampullary Regions in MRI Images Using Deep Learning

Abstract

Background: Development and validation of a deep learning method to automatically segment the peri-ampullary (PA) region in magnetic resonance imaging (MRI) images. Methods: A group of patients with or without periampullary carcinoma (PAC) was included. The PA regions were manually annotated in MRI images by experts. Patients were randomly divided into one training set and one validation set. A deep learning method to automatically segment the PA region in MRI images was developed using the training set. The segmentation performance of the method was evaluated in the validation set. Results: The deep learning algorithm achieved optimal accuracies in the segmentation of the PA regions in both T1 and T2 MRI images. The value of the intersection over union (IoU) was 0.67 and 0.68 for T1 and T2 images, respectively. Conclusions: Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the PA region in MRI images. This automated non-invasive method helps clinicians to identify and locate the PA region using preoperative MRI scanning.