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12 June 2020 Lung tumor detection using PET/CT scanning based on multiscale and multimodality Mask R-CNN
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Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115190F (2020) https://doi.org/10.1117/12.2573813
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan
Abstract
Positron emission tomography/Computed Tomography (PET/CT) scanning is viewed as one of most effective technologies for lung tumor diagnosis. However, with increasing application of PET/CT scanning, massive medical images were generated, and radiologists are suffering an increasing workload. Hence, it could effectively curtail the workload of radiologist by developing the Computer-aided Diagnosis method based on Artificial Intelligence technology. In this paper, a novel approach for lung tumor detection using PET/CT imaging based on multiscale and multimodality Mask Region-based Convolutional Neural Network (Mask R-CNN) was proposed. We firstly produced three Mask R- CNN models to extract lung tumor candidate. Of the three models, two were trained using PET images with two different scales, and the third one was trained using CT images. Each training dataset included 400 lung tumor slices. Then these three models were combined using method of intergrated learning. 160 axial slices, including 50 PET slices and 50 CT slices with lung tumor and 30 PET normal slices and 30 CT normal slices, were employed for testing. The F- score, precision and recall of novel method were 0.94, 0.92, and 0.98, respectively. The experimental results showed that the proposed method has the capbility of detecting lung tumor effectively and identifying a healthy chest pattern
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rui Zhang, Chao Cheng, Wen Hu, Xuechen Li, and Changjing Zuo "Lung tumor detection using PET/CT scanning based on multiscale and multimodality Mask R-CNN", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115190F (12 June 2020); https://doi.org/10.1117/12.2573813
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