ArticleScientific reports2019
Segmentation of Heavily Clustered Nuclei from Histopathological Images.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Application of Cumulative Threshold Approaches to Tissue Iron Measurement on Histochemical or Synchrotron X‑ray Fluorescence Platforms.Chemical & biomedical imaging · 2026Article
- High-frequency energy fusion (HFEF) for nuclei segmentation with boundary-aware loss.Quantitative imaging in medicine and surgery · 2026Article
- Clopidogrel Administration Impairs Neurovascular Unit Recovery and Exacerbates Amyloid Beta Accumulation in Aged Mice Post-Stroke.International journal of molecular sciences · 2026Article
- Bioimaging With Fluorescent Nucleic-Acid Aptamers for the Specific Detection and Quantification of Pseudomonas aeruginosa Alone and in Heterogeneous Bacterial Populations.MicrobiologyOpen · 2025Article
- ImmuNet: a segmentation-free machine learning pipeline for immune landscape phenotyping in tumors by multiplex imaging.Biology methods & protocols · 2025Article
- Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets.Journal of imaging informatics in medicine · 2024Review
- Optimizing deep learning-based segmentation of densely packed cells using cell surface markers.BMC medical informatics and decision making · 2024Article
- High resolution spatial profiling of kidney injury and repair using RNA hybridization-based in situ sequencing.Nature communications · 2024Article
- Software BreastAnalyser for the semi-automatic analysis of breast cancer immunohistochemical images.Scientific reports · 2024Article
- Recurrence risk stratification of hepatocellular carcinomas based on immune gene expression and features extracted from pathological images.PLoS computational biology · 2023Article
- Optimizing Deep Learning-Based Segmentation of Densely Packed Cells using Cell Surface Markers.Research square · 2023Article
- Breast Cancer Histopathological Images Segmentation Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images.Biomimetics (Basel, Switzerland) · 2023Article
- Clopidogrel Administration Impairs Post-Stroke Learning and Memory Recovery in Mice.International journal of molecular sciences · 2023Article
- Grading of lung adenocarcinomas with simultaneous segmentation by artificial intelligence (GLASS-AI).NPJ precision oncology · 2023Article
- A survey on recent trends in deep learning for nucleus segmentation from histopathology images.Evolving systems · 2023Review
- Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection.Journal of biomedical informatics · 2023Article
- Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- An integrative approach of digital image analysis and transcriptome profiling to explore potential predictive biomarkers for TGFActa pharmaceutica Sinica. B · 2022Article
- Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force.Scientific reports · 2022Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Automated cell nucleus segmentation is the key to gain further insight into cell features and functionality which support computer-aided pathology in early diagnosis of diseases such as breast cancer and brain tumour. Despite considerable advances in automated segmentation, it still remains a challenging task to split heavily clustered nuclei due to intensity variations caused by noise and uneven absorption of stains. To address this problem, we propose a novel method applicable to variety of histopathological images stained for different proteins, with high speed, accuracy and level of automation. Our algorithm is initiated by applying a new locally adaptive thresholding method on watershed regions. Followed by a new splitting technique based on multilevel thresholding and the watershed algorithm to separate clustered nuclei. Finalized by a model-based merging step to eliminate oversegmentation and a model-based correction step to improve segmentation results and eliminate small objects. We have applied our method to three image datasets: breast cancer stained for hematoxylin and eosin (H&E), Drosophila Kc167 cells stained for DNA to label nuclei, and mature neurons stained for NeuN. Evaluated results show our method outperforms the state-of-the-art methods in terms of accuracy, precision, F1-measure, and computational time.
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