Image segmentation has been increasingly applied in medical settings as recent developments have skyrocketed the potential applications of deep learning. Urology, specifically, is one field of medicine that is primed for the adoption of a real-time image segmentation system with the long-term aim of automating endoscopic stone treatment. In this project, we explored supervised deep learning models to annotate kidney stones in surgical endoscopic video feeds. In this paper, we describe how we built a dataset from the raw videos and how we developed a pipeline to automate as much of the process as possible. For the segmentation task, we adapted and analyzed three baseline deep learning models -- U-Net, U-Net++, and DenseNet -- to predict annotations on the frames of the endoscopic videos with the highest accuracy above 90\%. To show clinical potential for real-time use, we also confirmed that our best trained model can accurately annotate new videos at 30 frames per second. Our results demonstrate that the proposed method justifies continued development and study of image segmentation to annotate ureteroscopic video feeds.
翻译:在医学环境中,由于最近的发展,在医学环境中越来越多地应用图象分割法,因为最近的发展使深层学习的潜在应用急剧增加。具体地说,泌尿学是一个医学领域,准备采用实时图象分割法,其长期目标是使内分层石处理自动化。在这个项目中,我们探索了在外科内分层视频材料中对肾结石进行批注的有监督的深层次学习模型。在本文中,我们描述了我们如何从原始视频中建立数据集,以及如何发展一条管道,尽可能使这一过程自动化。在分层工作方面,我们调整和分析了三种基线深层学习模型 -- -- U-Net、U-Net++和DenseNet -- 以预测内分层视频框架的注释,其精度高于90 ⁇ 。为了展示实时使用的临床潜力,我们还确认,我们最训练有素的模型可以精确地在每秒30个框架对新视频进行注解。我们的结果表明,拟议方法证明有必要继续开发和研究图象分解图象,以图象源。