Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

Abstract The assessment of glomerular lesions is a fundamental step toward the diagnosis of glomerular diseases. This requires diagnosis and fusion of information from all the glomeruli at multiple levels and stainings. The lack of research on multi‐level multistained glomerular identification and matching has resulted in renal pathologists devoting much time and attention to this time‐consuming and labor‐intensive process. This limits the overall efficiency of the diagnosis of glomerular diseases. This paper constructed a dataset consisting of 600 cases, each containing 12 levels of whole slide images from H&E, PAS, Masson trichrome, and PASM staining. The glomeruli identifying and matching was proposed. First, a multistained transformer‐based Mask R‐CNN is proposed to extract the position and contours of the glomeruli. Second, coherent point drift‐based coarse matching and hybrid feature‐based fine matching achieve pairwise matching. Finally, the voting‐based cross‐matching realizes 12‐level multistained matching. This system constitutes a practical human‐computer interface. Intensive experiments were conducted to validate the ability to identify and match 12‐level multistained glomeruli. The mAP@50 for detection and segmentation reached 95.40% and 95.70%, respectively. The basic and comprehensive matching rates of the glomeruli matching reached 98.25% and 74.59%, respectively. Visualization results further demonstrate that the model achieved accurate identification and matching. The proposed system achieves accurate identification and matching of 12 levels of multistained glomeruli and can serve as a tool for pathological diagnosis of glomerular diseases by simplifying the diagnostic process. More importantly, this system can lay the foundation for the fully automated assisted diagnosis of glomerular diseases.