Automated CT Pancreas Segmentation for Acute Pancreatitis Patients by combining a Novel Object Detection Approach and U-Net

Authors

Yang Deng
Lan Lan
Lei You
Kai Chen
Li Peng
Weiling Zhao
Bin Song
Yi Wang
Zhiwei Ji
Xiaobo Zhou

Publication Date

3-1-2023

Journal

Biomedical Signal Processing and Control

Abstract

Acute pancreatitis is an inflammatory disorder of the pancreas. Medical imaging, such as computed tomography (CT), has been widely used to detect volume changes in the pancreas for acute pancreatitis diagnosis. Many pancreas segmentation methods have been proposed but no methods for pancreas segmentation from acute pancreatitis patients. The segmentation of an inflamed pancreas is more challenging than the normal pancreas due to the following two reasons. 1) The inflamed pancreas invades surrounding organs and causes blurry boundaries. 2) The inflamed pancreas has higher shape, size, and location variability than the normal pancreas. To overcome these challenges, we propose an automated CT pancreas segmentation approach for acute pancreatitis patients by combining a novel object detection approach and U-Net. Our approach includes a detector and a segmenter. Specifically, we develop an FCN-guided region proposal network (RPN) detector to localize the pancreatitis regions. The detector first uses a fully convolutional network (FCN) to reduce the background interference of medical images and generates a fixed feature map containing the acute pancreatitis regions. Then the RPN is employed on the feature map to precisely localize the acute pancreatitis regions. After obtaining the location of pancreatitis, the U-Net segmenter is used on the cropped image according to the bounding box. The proposed approach is validated using a collected clinical dataset with 89 abdominal contrast-enhanced 3D CT scans from acute pancreatitis patients. Compared with other start-of-the-art approaches for normal pancreas segmentation, our method achieves better performance on both localization and segmentation in acute pancreatitis patients.

Keywords

Region proposal network, object detection, acute pancreatitis, CT segmentation

DOI

10.1016/j.bspc.2022.104430

PMID

37304128

PMCID

PMC10249746

PubMedCentral® Posted Date

3-1-2024

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes