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Membranous nephropathy (MN) is a common pathological type of nephrotic syndrome. The characteristic of MN is the presence of immune complex deposits containing 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 EDDs using the core operator DCNv3. 2) The detector utilizes MSDAM as the attention mechanism to detect EDDs of different sizes and locations effectively. The proposed Deformable R-CNN was tested on the 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.

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