ArticlePloS one2016
Automated Segmentation of Nuclei in Breast Cancer Histopathology Images.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Active Contours Connected Component Analysis Segmentation Method of Cancerous Lesions in Unsupervised Breast Histology Images.Bioengineering (Basel, Switzerland) · 2025Article
- A color-based tumor segmentation method for clinical ex vivo breast tissue assessment utilizing a multi-contrast brightfield imaging strategy.Journal of biophotonics · 2024Article
- Breast Cancer Histopathological Images Segmentation Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Review
- Singular Nuclei Segmentation for AutomaticSensors (Basel, Switzerland) · 2022Article
- Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review.Journal of medical systems · 2021Review
- Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review.Frontiers in oncology · 2021Review
- A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images.Medical & biological engineering & computing · 2019Article
- Superpixel-Based Conditional Random Fields (SuperCRF): Incorporating Global and Local Context for Enhanced Deep Learning in Melanoma Histopathology.Frontiers in oncology · 2019Article
- A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring.Sensors (Basel, Switzerland) · 2018Article
- Multi-radial LBP Features as a Tool for Rapid Glomerular Detection and Assessment in Whole Slide Histopathology Images.Scientific reports · 2018Article
- Comparative Study on Automated Cell Nuclei Segmentation Methods for Cytology Pleural Effusion Images.Journal of healthcare engineering · 2018Article
- Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis.Scientific reports · 2017Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods-Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.