ArticleComputational and mathematical methods in medicine2019
Automatic Segmentation of Pathological Glomerular Basement Membrane in Transmission Electron Microscopy Images with Random Forest Stacks.
Article in Computational and mathematical methods in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 20 citations in OpenAlex.
- Renal-AI: A Deep Learning Platform for Multi-Scale Detection of Renal Ultrastructural Features in Electron Microscopy Images.Diagnostics (Basel, Switzerland) · 2026Article
- AI-Based System for Analysis of Electron Microscope Images in Glomerular Disease.JAMA network open · 2025Article
- A Self-Adaptive Strip Pooling Network for Segmenting the Kidney Glomerular Basement Membrane.Sensors (Basel, Switzerland) · 2025Article
- Digital pathology assessment of kidney glomerular filtration barrier ultrastructure in an animal model of podocytopathy.Biology methods & protocols · 2025Article
- Early automated detection system for skin cancer diagnosis using artificial intelligent techniques.Scientific reports · 2024Article
- Development of an automated estimation of foot process width using deep learning in kidney biopsies from patients with Fabry, minimal change, and diabetic kidney diseases.Kidney international · 2024Article
- Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum.Journal of pathology informatics · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Pathological classification through transmission electron microscopy (TEM) is essential for the diagnosis of certain nephropathy, and the changes of thickness in glomerular basement membrane (GBM) and presence of immune complex deposits in GBM are often used as diagnostic criteria. The automatic segmentation of the GBM on TEM images by computerized technology can provide clinicians with clear information about glomerular ultrastructural lesions. The GBM region on the TEM image is not only complicated and changeable in shape but also has a low contrast and wide distribution of grayscale. Consequently, extracting image features and obtaining excellent segmentation results are difficult. To address this problem, we introduce a random forest- (RF-) based machine learning method, namely, RF stacks (RFS), to realize automatic segmentation. Specifically, this work proposes a two-level integrated RFS that is more complicated than a one-level integrated RF to improve accuracy and generalization performance. The integrated strategies include training integration and testing integration. Training integration can derive a full-view RFS
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