ArticleJournal of medical systems2018
An End-to-End System for Automatic Urinary Particle Recognition with Convolutional Neural Network.
Article in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- DEM study on the effect of fiber length and content on the macro-micro mechanical behavior of fiber-sand mixture.Scientific reports · 2026Article
- A multi-head YOLOv12 with self-supervised pretraining for urinary sediment particle detection.Scientific reports · 2025Article
- Deep Learning-Based Culture-Free Bacteria Detection in Urine Using Large-Volume Microscopy.Biosensors · 2024Article
- Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction.Journal of digital imaging · 2023Article
- Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning classification of urinary sediment crystals with optimal parameter tuning.Scientific reports · 2022Article
- Rapid Detection of Urinary Tract Infection in 10 min by Tracking Multiple Phenotypic Features in a 30 s Large-Volume Scattering Video of Urine Microscopy.ACS sensors · 2022Article
- Morphological components detection for super-depth-of-field bio-micrograph based on deep learning.Microscopy (Oxford, England) · 2022Article
- Article
- Urine Sediment Recognition Method Based on Multi-View Deep Residual Learning in Microscopic Image.Journal of medical systems · 2019Article
- Deep learning approach to peripheral leukocyte recognition.PloS one · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The urine sediment analysis of particles in microscopic images can assist physicians in evaluating patients with renal and urinary tract diseases. Manual urine sediment examination is labor-intensive, subjective and time-consuming, and the traditional automatic algorithms often extract the hand-crafted features for recognition. Instead of using the hand-crafted features, in this paper we propose to exploit convolutional neural network (CNN) to learn features in an end-to-end manner to recognize the urinary particle. We treat the urinary particle recognition as object detection and exploit two state-of-the-art CNN-based object detection methods, Faster R-CNN and single shot multibox detector (SSD), along with their variants for urinary particle recognition. We further investigate different factors involving these CNN-based methods to improve the performance of urinary particle recognition. We comprehensively evaluate these methods on a dataset consisting of 5,376 annotated images corresponding to 7 categories of urinary particle, i.e., erythrocyte, leukocyte, epithelial cell, crystal, cast, mycete, epithelial nuclei, and obtain a best mean average precision (mAP) of 84.1% while taking only 72 ms per image on a NVIDIA Titan X GPU.
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Registered trials
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