Kim Kwang Baek, Song Doo Heon, Park Hyun Jun
Department of Artificial Intelligence, Silla University, Busan 46958, Korea.
Department of Computer Games, Yong-In Art and Science University, Yongin 17145, Korea.
Diagnostics (Basel). 2021 Dec 10;11(12):2329. doi: 10.3390/diagnostics11122329.
Ganglion cysts are common soft tissue masses of the hand and wrist, and small size cysts are often hypoechoic. Thus, identifying them from ultrasonography is not an easy problem. In this paper, we propose an automatic segmentation method using two artificial intelligence algorithms in sequence. A density based unsupervised learning algorithm called DBSCAN is performed as a front-end and its result determines the number of clusters used in the Fuzzy C-Means (FCM) clustering algorithm for quantification of ganglion cyst object. In an experiment using 120 images, the proposed method shows a higher extraction rate (89.2%) and lower false positive rate compared with FCM when the ground truth is set as the human expert's decision. Such human-like behavior is more apparent when the size of the ganglion cyst is small that the quality of ultrasonography is often not very high. With this fully automatic segmentation method, the operator subjectivity that is highly dependent on the experience of the ultrasound examiner can be mitigated with high reliability.
腱鞘囊肿是手部和腕部常见的软组织肿块，小尺寸囊肿通常为低回声。因此，通过超声检查识别它们并非易事。在本文中，我们提出了一种依次使用两种人工智能算法的自动分割方法。一种名为DBSCAN的基于密度的无监督学习算法作为前端执行，其结果决定了用于模糊C均值（FCM）聚类算法中腱鞘囊肿对象量化的聚类数量。在使用120幅图像的实验中，当将人类专家的判定作为真实标准时，与FCM相比，所提出的方法显示出更高的提取率（89.2%）和更低的假阳性率。当腱鞘囊肿尺寸较小时，这种类似人类的行为更为明显，因为此时超声检查的质量通常不是很高。通过这种全自动分割方法，可以高度可靠地减轻高度依赖超声检查人员经验的操作者主观性。
