ArticlePloS one2015
A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- Age-dependent changes in epidermal architecture explored using an automated image analysis algorithm on in vivo reflectance confocal microscopy images.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2023Article
- Automatic granular and spinous epidermal cell identification and analysis onJournal of biomedical optics · 2023Article
- Automating reflectance confocal microscopy image analysis for dermatological research: a review.Journal of biomedical optics · 2022Review
- Automated mesenchymal stem cell segmentation and machine learning-based phenotype classification using morphometric and textural analysis.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Algorithm to quantify nuclear features and confidence intervals for classification of oral neoplasia from high-resolution optical images.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Noninvasive diagnostic adjuncts for the evaluation of potentially premalignant oral epithelial lesions: current limitations and future directions.Oral surgery, oral medicine, oral pathology and oral radiology · 2018Review
- A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring.Sensors (Basel, Switzerland) · 2018Article
- Wavelet-based statistical classification of skin images acquired with reflectance confocal microscopy.Biomedical optics express · 2017Article
- Handheld tunable focus confocal microscope utilizing a double-clad fiber coupler for in vivo imaging of oral epithelium.Journal of biomedical optics · 2017Article
- A novel multimodal optical imaging system for early detection of oral cancer.Oral surgery, oral medicine, oral pathology and oral radiology · 2016Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Automatic segmentation of nuclei in reflectance confocal microscopy images is critical for visualization and rapid quantification of nuclear-to-cytoplasmic ratio, a useful indicator of epithelial precancer. Reflectance confocal microscopy can provide three-dimensional imaging of epithelial tissue in vivo with sub-cellular resolution. Changes in nuclear density or nuclear-to-cytoplasmic ratio as a function of depth obtained from confocal images can be used to determine the presence or stage of epithelial cancers. However, low nuclear to background contrast, low resolution at greater imaging depths, and significant variation in reflectance signal of nuclei complicate segmentation required for quantification of nuclear-to-cytoplasmic ratio. Here, we present an automated segmentation method to segment nuclei in reflectance confocal images using a pulse coupled neural network algorithm, specifically a spiking cortical model, and an artificial neural network classifier. The segmentation algorithm was applied to an image model of nuclei with varying nuclear to background contrast. Greater than 90% of simulated nuclei were detected for contrast of 2.0 or greater. Confocal images of porcine and human oral mucosa were used to evaluate application to epithelial tissue. Segmentation accuracy was assessed using manual segmentation of nuclei as the gold standard.
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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.