ArticleIEEE transactions on bio-medical engineering2012
Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set.
Article in IEEE transactions on bio-medical engineering, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed.
- Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis.Journal for immunotherapy of cancer · 2018Trial
- Enhancing Translation of H&E to IHC with Robust Tumor-Infiltrating Lymphocytes Quantification Using Deep Generative Models: A Feasibility Study.Journal of imaging informatics in medicine · 2026Article
- Article
- Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling.Bioengineering (Basel, Switzerland) · 2025Article
- Abnormality-aware multimodal learning for WSI classification.Frontiers in medicine · 2025Article
- Deep learning framework for automated goblet cell density analysis in in-vivo rabbit conjunctiva.Scientific reports · 2023Article
- A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue.Sensors (Basel, Switzerland) · 2023Article
- Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications.Cancers · 2022Review
- Automatic Whole Slide Pathology Image Diagnosis Framework via Unit Stochastic Selection and Attention Fusion.Neurocomputing · 2021Article
- A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability.Medical & biological engineering & computing · 2021Article
- Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review.Frontiers in oncology · 2021Review
- piNET-An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images.Cancers · 2020Article
- Fusion of whole and part features for the classification of histopathological image of breast tissue.Health information science and systems · 2020Article
- Interactive thyroid whole slide image diagnostic system using deep representation.Computer methods and programs in biomedicine · 2020Article
- A deep learning-based framework for lung cancer survival analysis with biomarker interpretation.BMC bioinformatics · 2020Article
- Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison.BMC bioinformatics · 2019Review
- Separating Touching Cells Using Pixel Replicated Elliptical Shape Models.IEEE transactions on medical imaging · 2019Article
- Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association.Journal of pathology informatics · 2019Review
- Effective nuclei segmentation with sparse shape prior and dynamic occlusion constraint for glioblastoma pathology images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
3 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Automated image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of breast cancer. Automated segmentation of the cells comprising imaged tissue microarrays (TMAs) is a prerequisite for any subsequent quantitative analysis. Unfortunately, crowding and overlapping of cells present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel algorithm that can reliably separate touching cells in hematoxylin-stained breast TMA specimens that have been acquired using a standard RGB camera. The algorithm is composed of two steps. It begins with a fast, reliable object center localization approach that utilizes single-path voting followed by mean-shift clustering. Next, the contour of each cell is obtained using a level set algorithm based on an interactive model. We compared the experimental results with those reported in the most current literature. Finally, performance was evaluated by comparing the pixel-wise accuracy provided by human experts with that produced by the new automated segmentation algorithm. The method was systematically tested on 234 image patches exhibiting dense overlap and containing more than 2200 cells. It was also tested on whole slide images including blood smears and TMAs containing thousands of cells. Since the voting step of the seed detection algorithm is well suited for parallelization, a parallel version of the algorithm was implemented using graphic processing units (GPU) that resulted in significant speedup over the C/C++ implementation.
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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.