Advanced Faster RCNN: a non-contrast CT-based algorithm for detecting pancreatic lesions in multiple disease stages / 南方医科大学学报
Journal of Southern Medical University
;
(12): 755-763, 2023.
Artigo
em Chinês
| WPRIM
| ID: wpr-986986
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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Pâncreas
/
Neoplasias Pancreáticas
/
Algoritmos
/
Tomografia Computadorizada por Raios X
/
China
Limite:
Humanos
País/Região como assunto:
Ásia
Idioma:
Chinês
Revista:
Journal of Southern Medical University
Ano de publicação:
2023
Tipo de documento:
Artigo
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