Minoura Natsuki, Teramoto Atsushi, Ito Akari, Yamamuro Osamu, Nishio Masami, Saito Kuniaki, Fujita Hiroshi
Graduate School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi, 470-1192, Japan.
Nagoya City University Hospital, Nagoya, Japan.
Radiol Phys Technol. 2019 Sep;12(3):260-267. doi: 10.1007/s12194-019-00516-8. Epub 2019 May 25.
In this study, we aimed to develop a hybrid method for automated detection of high-uptake regions in the breast and axilla using dedicated breast positron-emission tomography (db PET) and whole-body PET/computed tomography (CT) images. In our proposed method, high-uptake regions in the breast and axilla were detected using db PET images and whole-body PET/CT images. In db PET images, high-uptake regions in the breast were detected using adaptive thresholding technique based on the noise characteristics. In whole-body PET/CT images, the region of the breast that includes the axilla was first extracted using CT images. Next, high-uptake regions in the extracted breast region were detected on the PET images. By integration of the results of the two types of PET images, a final candidate region was obtained. In the experiments, the accuracy of extracting the region of the breast and detection ability was evaluated using clinical data. As a result, all breast regions were extracted correctly. The sensitivity of detection was 0.765, and the number of false positive cases were 1.8, which was 30% better than those on whole-body PET/CT alone. These results suggested that the proposed method, combining the two types of PET images is effective for improving detection performance.
在本研究中，我们旨在开发一种混合方法，用于使用专用乳腺正电子发射断层扫描（db PET）和全身PET/计算机断层扫描（CT）图像自动检测乳腺和腋窝中的高摄取区域。在我们提出的方法中，使用db PET图像和全身PET/CT图像检测乳腺和腋窝中的高摄取区域。在db PET图像中，基于噪声特征使用自适应阈值技术检测乳腺中的高摄取区域。在全身PET/CT图像中，首先使用CT图像提取包括腋窝的乳腺区域。接下来，在PET图像上检测提取的乳腺区域中的高摄取区域。通过整合两种类型PET图像的结果，获得最终的候选区域。在实验中，使用临床数据评估提取乳腺区域的准确性和检测能力。结果，所有乳腺区域均被正确提取。检测灵敏度为0.765，假阳性病例数为1.8，比单独使用全身PET/CT的情况好30%。这些结果表明，结合两种类型PET图像的所提出方法对于提高检测性能是有效的。
