Deformable R-CNN for Detection of Electron Dense Deposits in Glomerular Transmission Electron Microscopy Images

Membranous nephropathy (MN) is a common pathological type of nephrotic syndrome. The characteristic of MN is the presence of immune complex deposits containing immunoglobulin G (IgG), called electron-dense deposits (EDD), which can be observed by transmission electron microscopy (TEM). Quantitative analysis of the morphology and location of EDD can provide an essential reference for diagnosing and staging MN. However, accurately identifying and quantifying EDD is challenging due to their different morphologies, sizes, and locations with varying amounts. This paper proposes a two-stage Deformable R-CNN detector that overcomes these challenges, which has two characteristics: 1) The detector employs InternImage as the feature extractor, which extracts different morphological features of EDD using the core operator deformable convolution v3 (DCNv3). 2) The detector utilizes the multi-scale deformable attention model (MSDAM) as the attention mechanism to detect EDD of different sizes and locations effectively. The proposed Deformable R-CNN was tested on the Electron-Dense Deposit Detection for Membranous Nephropathy (EDDD-MN) dataset and outperformed other popular detectors, including two-stage, one-stage, and transformer-based detectors in detection and quantification. It also exhibited excellent performance in TIDE error analysis. Thus, this method would enable precise detection and rapid quantification of EDD, thereby reducing the workload of pathologists and helping them gain a comprehensive understanding of MN.