ArticleFrontiers in bioengineering and biotechnology2019
Methods for Segmentation and Classification of Digital Microscopy Tissue Images.
Article in Frontiers in bioengineering and biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.
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86 citing papers in PubMed, 19 citations in OpenAlex.
- Toward comprehensive cellular characterization of H&E slides.Journal of pathology informatics · 2026Article
- SiNuS: A comprehensive dataset for singular nuclei segmentation forData in brief · 2026Article
- NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- NS-GUSL: Green U-Shaped Learning for Nuclei Segmentation from Histopathology Images.Journal of imaging · 2026Article
- Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient Pathological Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- Distinct Tumor-Immune Ecologies in Patients with Lung Cancer Predict Progression and Define a Clinical Biomarker of Therapy Response.Cancer research · 2026Article
- Staining normalization in histopathology: Method benchmarking using multicenter dataset.Scientific reports · 2026Article
- Plant Microtechnique with Resin: Towards Plant Histolomics.Plants (Basel, Switzerland) · 2026Article
- FPNuNet: a frequency-aware prompt-guided network for nuclear segmentation and classification in immunohistochemistry images.GigaScience · 2026Article
- Histo-Miner: Deep learning based tissue features extraction pipeline from H&E whole slide images of cutaneous squamous cell carcinoma.PLoS computational biology · 2026Article
- CellSAM: a foundation model for cell segmentation.Nature methods · 2025Article
- ClinSegNet: Towards Reliable and Enhanced Histopathology Screening.Bioengineering (Basel, Switzerland) · 2025Article
- Evidential deep learning-based ALK-expression screening using H&E-stained histopathological images.NPJ digital medicine · 2025Article
- Computer vision to predict cell seeding coverage in re-endothelialized mouse lungs.Scientific reports · 2025Article
- AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology.NPJ digital medicine · 2025Article
- Instance-level semantic segmentation of nuclei based on multimodal structure encoding.BMC bioinformatics · 2025Article
- Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling.Bioengineering (Basel, Switzerland) · 2025Article
- HistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification.BMC medical imaging · 2025Article
- Nuclei segmentation and classification from histopathology images using federated learning for end-edge platform.PloS one · 2025Article
- Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization.Computational and structural biotechnology journal · 2024Article
26 more citing papers are in PubMed but not listed here.
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Authors and funding
15 authors at 6 institutions in 3 countries.
Funding
Abstract
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.