Video to All-in-focus Image Reconstruction Algorithm for Automated Microscopic Urinalysis

2026-07-15

AI Summary

This study addresses the inefficiency of conventional microscopic urinalysis, which requires acquiring multiple focal images due to the multilayered structure of urine samples, resulting in a cumbersome and time-consuming workflow. To overcome this limitation, the authors propose a novel approach that replaces traditional multi-focus image acquisition with a short manually focused video sequence. From this video, an all-in-focus image is computationally reconstructed and subsequently analyzed using a deep learning model for automated detection and classification of urinary sediment components. Experimental evaluation on 14 real-world laboratory videos demonstrates that the proposed method significantly streamlines the analytical process while enhancing both the automation level and clinical practicality of urinalysis.

Abstract

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.

Problem

• microscopic urinalysis
• all-in-focus image
• multi-layer structure
• depth of field
• automated diagnosis

Innovation

• all-in-focus reconstruction
• video-based focusing
• automated urinalysis
• deep learning
• microscopic imaging