Advanced Faster RCNN: a non-contrast CT-based algorithm for detecting pancreatic lesions in multiple disease stages.
10.12122/j.issn.1673-4254.2023.05.11
- Author:
Lidu LIANG
1
;
Haojie ZHANG
2
;
Qian LU
3
;
Chenjie ZHOU
2
;
Shulong LI
1
Author Information
1. School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.
2. General Surgery Center, Second Department of Hepatobiliary Surgery, Guangdong Provincial Research Center for Artificial Organ and Tissue Engineering, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China.
3. Department of Ultrasound, Yancheng Third People's Hospital, Yancheng 224008, China.
- Publication Type:Journal Article
- Keywords:
lesion detection;
non-contrast CT;
pancreatic cancer
- MeSH:
Humans;
Pancreas/diagnostic imaging*;
Algorithms;
China;
Pancreatic Neoplasms/diagnostic imaging*;
Tomography, X-Ray Computed
- From:
Journal of Southern Medical University
2023;43(5):755-763
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To propose a non-contrast CT-based algorithm for automated and accurate detection of pancreatic lesions at a low cost.
METHODS:With Faster RCNN as the benchmark model, an advanced Faster RCNN (aFaster RCNN) model for pancreatic lesions detection based on plain CT was constructed. The model uses the residual connection network Resnet50 as the feature extraction module to extract the deep image features of pancreatic lesions. According to the morphology of pancreatic lesions, 9 anchor frame sizes were redesigned to construct the RPN module. A new Bounding Box regression loss function was proposed to constrain the training process of RPN module regression subnetwork by comprehensively considering the constraints of the lesion shape and anatomical structure. Finally, a detection frame was generated using the detector in the second stage. The data from a total of 728 cases of pancreatic diseases from 4 clinical centers in China were used for training (518 cases, 71.15%) and testing (210 cases, 28.85%) of the model. The performance of aFaster RCNN was verified through ablation experiments and comparison experiments with 3 classical target detection models SSD, YOLO and CenterNet.
RESULTS:The aFaster RCNN model for pancreatic lesion detection achieved recall rates of 73.64% at the image level and 92.38% at the patient level, with an average precision of 45.29% and 53.80% at the image and patient levels, respectively, which were higher than those of the 3 models for comparison.
CONCLUSION:The proposed method can effectively extract the imaging features of pancreatic lesions from non-contrast CT images to detect the pancreatic lesions.
