Identifying Periampullary Regions in MRI Images Using Deep Learning

Yong Tang; Yingjun Zheng; Xinpei Chen; Weijia Wang; Qingxi Guo; Jian Shu; Jiali Wu; Song Su

doi:10.3389/fonc.2021.674579

Identifying Periampullary Regions in MRI Images Using Deep Learning

Front Oncol. 2021 May 28:11:674579. doi: 10.3389/fonc.2021.674579. eCollection 2021.

Authors

Yong Tang¹, Yingjun Zheng², Xinpei Chen³, Weijia Wang⁴, Qingxi Guo⁵, Jian Shu⁶, Jiali Wu⁷, Song Su²

Affiliations

¹ School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
² Department of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Luzhou, China.
³ Department of Hepatobiliary Surgery, Deyang People's Hospital, Deyang, China.
⁴ School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China.
⁵ Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
⁶ Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
⁷ Department of Anesthesiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.

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, one validation set, and one test set. Deep learning methods were developed to automatically segment the PA region in MRI images. The segmentation performance of the methods was compared in the validation set. The model with the highest intersection over union (IoU) was evaluated in the test 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 IoU was 0.68, 0.68, and 0.64 for T1, T2, and combination of 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.

Keywords: MRI; deep learning; peri-ampullary cancer; periampullary regions; segmentation.