An End-to-End System for Automatic Urinary Particle Recognition with Convolutional Neural Network

作者：Liang, Yixiong^*; Kang, Rui; Lian, Chunyan; Mao, Yuan

来源：Journal of Medical Systems, 2018, 42(9): 165.

DOI：10.1007/s10916-018-1014-6

• Urinary particle recognition
• CNN
• Faster R-CNN
• SSD

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.

• 出版日期2018-9
• 单位中南大学