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This paper introduces a novel algorithm for reconstructing all-in-focus images from videos captured during microscopic urinalysis, addressing the challenge of multi-layered urine samples that traditional methods struggle to analyze. By recording a short video while manually adjusting the lens focus, the authors streamline the image acquisition process, significantly reducing the time required for diagnostics. Experimental results demonstrate the algorithm's effectiveness in accurately detecting and classifying urine sediments, showcasing its potential to enhance automated urinalysis workflows in clinical settings.
Capturing just a few seconds of video can transform the tedious task of urinalysis into a rapid, automated process that improves diagnostic accuracy.
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.
