期刊
MOLECULAR IMAGING
卷 18, 期 -, 页码 1-8出版社
HINDAWI LTD
DOI: 10.1177/1536012119863531
关键词
multiscale; mask R-CNN; ensemble learning; lung tumor; PET
资金
- Shenzhen Science and Technology Project [JCYJ20170306095702695, KJYY20170724152553858]
- Special Innovation Project of Guangdong Education Department [2017GkQNCX069]
- National Natural Science Foundation of China [61702337]
- Natural Science Foundation of Guangdong Province [2018A030313382, 2018A030313339]
Positron emission tomography (PET) imaging serves as one of the most competent methods for the diagnosis of various malignancies, such as lung tumor. However, with an elevation in the utilization of PET scan, radiologists are overburdened considerably. Consequently, a new approach of computer-aided diagnosis is being contemplated to curtail the heavy workloads. In this article, we propose a multiscale Mask Region-Based Convolutional Neural Network (Mask R-CNN)-based method that uses PET imaging for the detection of lung tumor. First, we produced 3 models of Mask R-CNN for lung tumor candidate detection. These 3 models were generated by fine-tuning the Mask R-CNN using certain training data that consisted of images from 3 different scales. Each of the training data set included 594 slices with lung tumor. These 3 models of Mask R-CNN models were then integrated using weighted voting strategy to diminish the false-positive outcomes. A total of 134 PET slices were employed as test set in this experiment. The precision, recall, and F score values of our proposed method were 0.90, 1, and 0.95, respectively. Experimental results exhibited strong conviction about the effectiveness of this method in detecting lung tumors, along with the capability of identifying a healthy chest pattern and reducing incorrect identification of tumors to a large extent.
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