The urine formed element instance segmentation based on YOLOv5n.

Accurate and efficient detection and segmentation of the urine formed element plays a vital role in the clinical diagnosis and treatment of many diseases, such as urinary system diseases, kidney diseases, and other diseases. However, artificial microscopy is subjective, and time- consuming. The mainstream detection and instance segmentation algorithms lack adequate accuracy and speed for the urine formed element due to small and dense targets. Therefore, this study proposes a quick one-stage urine formed element instance segmentation model based on YOLOv5n. The approach first employs a backbone architecture to extract features named shallow graphical features and semantic features from urine cells. Next, the neck network combines shallow graphical features with different deep semantic features, obtaining multi-scale, and multi-level features. Finally, according to these multi-level features, the head network of YOLOv5n integrates a small FCN network into the YOLOv5 detector. It obtains the location, classification, and segmentation results of the targets. To validate the superiority of this approach in terms of speed and accuracy, a special urine formed element dataset including 500 images was created. Experimental results show that the YOLOv5n method achieves a Mean Average Precision (mAP) at intersection over the union threshold of 0.5 (mAP50) with 91.8%, and Frames Per Second (FPS) of 63.3. Compared to Mask R-CNN and YOLOv8, its FPS increased by 62.6 and 60.9, respectively, resulting in nearly a hundred-fold speedup, and its mAP50 also increased by 3.6 and 1.4% points in accuracy, respectively. Additionally, the YOLOv5n obtains a superior balance of accuracy and speed in comparisons with SOLOv2, BoxInst, and ConvNeXt V2. This study developed a new automated analysis of urinary particles based on deep learning, and this method is expected to be used for the automated analysis and detection of the urine formed element. The experimental results also demonstrate that YOLOv5n can achieve more accurate and faster instance segmentation of urine formed element, providing technical support for clinical disease diagnosis.