One-stage pulmonary nodule detection using 3-D DCNN with feature fusion and attention mechanism in CT image. (June 2022)

• Record Type: Journal Article
• Authors: Huang, Yao-Sian; Chou, Ping-Ru; Chen, Hsin-Ming; Chang, Yeun-Chung; Chang, Ruey-Feng
• Is Part Of: Computer methods and programs in biomedicine. Volume 220(2022)
• Publication Date: 2022-06
• Subjects: Lung nodule detection -- Computer-aided detection -- YOLO -- Receptive field block -- One-shot aggregation -- Feature fusion scheme; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106786
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A fast and efficient computer-aided tumor detection (CADe) based on one-stage 3-D convolutional neural network (CNN) detection model is used for automatic nodule detection. The attention mechanism, special feature fusion, and auxiliary path are employed and proposed to improve the performance of one-stage detection model. Compared the previous studies, our CADe with the employed methods can provide the more reliable detection results. In addition, without the false positive (FP) reduction, the competition performance metric (CPM) of our system is over 0.9. Abstract: Background and objective: Lung cancer is the most common cause of cancer-related death in the world. Low-dose computed tomography (LDCT) is a widely used modality in lung cancer detection. The nodule is an abnormal tissue and may evolve into lung cancer. Hence, it is crucial to detect nodules in the early detection stage. However, reviewing the LDCT scans to observe suspicious nodules is a time-consuming task. Recently, designing a computer-aided detection (CADe) system with convolutional neural network (CNN) architecture has been proven that it is helpful for radiologists. Hence, in this study, a 3-D YOLO-based CADe system, 3-D OSAF-YOLOv3, is proposed for nodule detection in LDCT images. Methods: The proposed CADe system consists of data preprocessing, nodule detection, and non-maximum suppression algorithm (NMS). At first, the data preprocessing including the background elimination, the spacing normalization, and the volume of interest (VOI) extraction, are conducted to remove the non-lung region, normalize the image spacing, and divide LDCT image into numerous VOIs. Then, the VOIs are fed into the 3-D OSAF-YOLOv3 model, to detect the suspicious nodules. The proposed model is constructed by integrating the 3-D YOLOv3 with the one-shot aggregation module (OSA), the receptive field block (RFB), and the feature fusion scheme (FFS). Finally, the NMS algorithm is performed to eliminate the duplicated detection generated by the model. Results: In this study, the LUNA-16 dataset composed 1186 nodules from 888 LDCT scans and the competition performance metric (CPM) are used to evaluate our CADe system. In the experiment results, the proposed system can achieve a sensitivities rate of 0.962 with the false positive rate of 8 and complete a CPM value of 0.905. Moreover, according to the ablation study results, the employment of OSA module, RFB, and FFS could improve the detection performance actually. Furthermore, compared to other start-of-the-art (SOTA) models, our detection system could also achieve the higher performance. Conclusions: In this study, a YOLO-based CADe system for nodule detection in CT image system integrating additional modules and scheme is proposed for nodule detection in LDCT. The result indicates that the proposed the modification can significantly improve detection performance.