Video to All-in-focus Image Reconstruction Algorithm for Automated Microscopic Urinalysis
For clinical labs, this simplifies automated urinalysis by replacing multiple image acquisitions with a single video, but the evaluation is limited to 14 videos, making it a preliminary proof of concept.
The paper proposes a method to reconstruct an all-in-focus image from a short video (2-14 seconds) of urine samples taken while manually changing focus, enabling automated detection and classification of urine sediments. Experiments on 14 videos demonstrate the effectiveness of the pipeline.
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.