Zhang Rui, Cheng Chao, Zhao Xuehua, Li Xuechen
1 Department of Digital Media, Shenzhen Institute of Information Technology, Shenzhen, Guangdong, China.
2 Department of Nuclear Medicine, Changhai Hospital, Shanghai, People's Republic of China.
Mol Imaging. 2019 Jan-Dec;18:1536012119863531. doi: 10.1177/1536012119863531.
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 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.
正电子发射断层扫描（PET）成像作为诊断各种恶性肿瘤（如肺癌）最有效的方法之一。然而，随着PET扫描利用率的提高，放射科医生的负担大大加重。因此，正在考虑一种新的“计算机辅助诊断”方法来减轻繁重的工作量。在本文中，我们提出了一种基于多尺度基于区域的卷积神经网络（Mask R-CNN）的方法，该方法使用PET成像来检测肺癌。首先，我们生成了3个用于肺癌候选检测的Mask R-CNN模型。这3个模型是通过使用由3种不同尺度的图像组成的特定训练数据对Mask R-CNN进行微调而生成的。每个训练数据集包括594个有肺癌的切片。然后，这3个Mask R-CNN模型使用加权投票策略进行整合，以减少假阳性结果。在该实验中，总共使用了134个PET切片作为测试集。我们提出的方法的精确率、召回率和F1分数值分别为0.90、1和0.95。实验结果有力地证明了该方法在检测肺癌方面的有效性，以及识别健康胸部模式并在很大程度上减少肿瘤误判的能力。
