Regional constraint consistency contrastive learning for automatic detection of urinary sediment in microscopic images

Abstract

Diagnosing renal and urinary system illnesses usually entails analysing the sediment found in urine. The components in microscopic urine images are diverse and show high similarity, with low contrast due to noise, impeding the progress of automated urine analysis. In order to tackle this difficulty, we propose a region-constrained consistency contrastive learning approach for automated urine analysis. In the first stage, we tackle the complex overlap phenomena in microscopic urine images by innovating the Urine Sediment Paste (US-Paste) positive sample construction method based on supervised contrastive learning. This method uses label information to apply regional constraints and improves the performance of out-of-distribution detection. We also rebuilt the Global Guidance Module (GG Module) and the Enhanced Supervision Module(ES Module). The former improves contrast in urine sediment images by restoring important image details guided by an encoder–decoder structure, while the latter achieves strong feature consistency by combining the most pertinent feature responses from four sets of attention feature maps, which are further mapped via a projection network. In the second phase, we enhance the representations acquired in the initial phase by incorporating a linear classification layer. Our region-constrained consistency contrastive learning algorithm attained an average classification accuracy of 98.30%, precision of 98.33%, recall of 98.30%, and F1-score of 98.30% on the private dataset. Furthermore, in the public urine sediment dataset, the approach achieved an average classification accuracy of 96.19%, precision of 95.79%, recall of 96.19%, and F1-score of 95.94%. The public chromosomal dataset yielded an average classification accuracy of 95.46%, precision of 94.84%, recall of 95.47%, and F1-score of 95.15%. Our methodology surpasses the most advanced methods and demonstrates exceptional performance in urine analysis. This showcases the efficiency of our label-based regional limitations, the outstanding out-of-distribution detection performance of US-Paste, and the robust feature consistency achieved by the Guided Supervision Encoder (GS Encoder). This substantially enhances diagnostic efficiency for clinicians and significantly advances the progress of automated urine sediment analysis.