发表机构
Birla Institute of Technology and Science Pilani; Institute of Mathematics and Image Computing; University of Lübeck（比拉理工大学和科学学院; 数学与图像计算研究所; 吕贝克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对显微镜尿液分析中获取高质量图像耗时问题，提出录制视频并重建全焦图像的方法，通过手动改变镜头焦点录制2到14秒视频来简化任务，经实验验证该自动尿液分析流程及新算法有效。
AI 中文摘要
显微镜尿液分析是医院的常规诊断测试。深度学习方法可实现尿液分析自动化，但需高质量图像，而实际玻片上尿液样本有多层结构，聚焦特定平面时细胞无法全看清。本文提出通过录制视频简化任务，手动逐渐改变镜头焦点，视频时长2到14秒。从视频帧重建全焦图像，应用深度学习模型检测和分类尿液沉积物。通过14个视频实验验证了该自动尿液分析流程及新重建算法的有效性。
英文摘要
Microscopic urinalysis is a routine diagnostic test at hospitals. Recent studies have demonstrated the effectiveness of deep learning methods to automate microscopic urinalysis. These methods rely on high-quality images of the urine samples in which each cell is clearly identifiable. However, in practice, the urine sample on a glass slide has a multi-layer structure; hence, all the cells are not clearly visible within the depth of field of a lens focused at a particular focal plane. It demands acquiring multiple images at different focal planes to correctly identify each cell in a given urine sample, which is a time-consuming task. In this paper, we propose to simplify the task by recording a video, in place of acquiring multiple images, while gradually changing the focus of the lens manually by hand. A typical length of the video is from 2 to 14 seconds. We reconstruct an all-in-focus image from the recorded video frames and apply a deep learning model to detect and classify urine sediments. As a proof of concept, we conduct experiments on 14 videos acquired by a trained lab technician in a usual diagnostic lab environment and show the effectiveness of the proposed automated urinalysis pipeline with our novel reconstruction algorithm.
