Kanauchi Yurie, Hashimoto Masahiro, Toda Naoki, Okamoto Saori, Haque Hasnine, Jinzaki Masahiro, Sakakibara Yasubumi
Department of Biosciences and Informatics, Keio University, Yokohama 2238522, Japan.
Department of Radiology, Keio University School of Medicine, Tokyo 1608582, Japan.
Healthcare (Basel). 2023 Feb 7;11(4):484. doi: 10.3390/healthcare11040484.
Ultrasonography is widely used for diagnosis of diseases in internal organs because it is nonradioactive, noninvasive, real-time, and inexpensive. In ultrasonography, a set of measurement markers is placed at two points to measure organs and tumors, then the position and size of the target finding are measured on this basis. Among the measurement targets of abdominal ultrasonography, renal cysts occur in 20-50% of the population regardless of age. Therefore, the frequency of measurement of renal cysts in ultrasound images is high, and the effect of automating measurement would be high as well. The aim of this study was to develop a deep learning model that can automatically detect renal cysts in ultrasound images and predict the appropriate position of a pair of salient anatomical landmarks to measure their size. The deep learning model adopted fine-tuned YOLOv5 for detection of renal cysts and fine-tuned UNet++ for prediction of saliency maps, representing the position of salient landmarks. Ultrasound images were input to YOLOv5, and images cropped inside the bounding box and detected from the input image by YOLOv5 were input to UNet++. For comparison with human performance, three sonographers manually placed salient landmarks on 100 unseen items of the test data. These salient landmark positions annotated by a board-certified radiologist were used as the ground truth. We then evaluated and compared the accuracy of the sonographers and the deep learning model. Their performances were evaluated using precision-recall metrics and the measurement error. The evaluation results show that the precision and recall of our deep learning model for detection of renal cysts are comparable to standard radiologists; the positions of the salient landmarks were predicted with an accuracy close to that of the radiologists, and in a shorter time.
超声检查因其无放射性、非侵入性、实时性和低成本，被广泛用于诊断内脏疾病。在超声检查中，会在两个点放置一组测量标记来测量器官和肿瘤，然后在此基础上测量目标发现物的位置和大小。在腹部超声检查的测量目标中，无论年龄大小，20%至50%的人群会出现肾囊肿。因此，超声图像中肾囊肿的测量频率很高，自动化测量的效果也会很高。本研究的目的是开发一种深度学习模型，该模型可以自动检测超声图像中的肾囊肿，并预测一对显著解剖标志的合适位置以测量其大小。深度学习模型采用微调后的YOLOv5来检测肾囊肿，并采用微调后的UNet++来预测显著图，显著图表示显著标志的位置。将超声图像输入到YOLOv5中，然后将在边界框内裁剪并由YOLOv5从输入图像中检测到的图像输入到UNet++中。为了与人类表现进行比较，三名超声医师在100个未见的测试数据项上手动放置显著标志。由一名经过委员会认证的放射科医生标注的这些显著标志位置被用作地面真值。然后，我们评估并比较了超声医师和深度学习模型的准确性。使用精确召回率指标和测量误差来评估它们的性能。评估结果表明，我们用于检测肾囊肿的深度学习模型的精确率和召回率与标准放射科医生相当；显著标志的位置预测准确率接近放射科医生，且用时更短。
