ArticleBiomedical optics express2022
Automated assessment of breast margins in deep ultraviolet fluorescence images using texture analysis.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Effective transfer of tumor annotations from hematoxylin and eosin to fluorescence images of breast and lung tissues.Journal of biomedical optics · 2026Article
- H&E Style Translation Using CycleGAN for Deep Ultraviolet-Excitation Fluorescence Images of Pancreatic Endoscopic Ultrasound-Fine Needle Aspiration/Biopsy Toward Slide-Free Rapid Pathology.Acta histochemica et cytochemica · 2025Article
- MALDI imaging combined with two-photon microscopy reveals local differences in the heterogeneity of colorectal cancer.Npj imaging · 2024Article
- DEEP LEARNING FOR AUTOMATED DETECTION OF BREAST CANCER IN DEEP ULTRAVIOLET FLUORESCENCE IMAGES WITH DIFFUSION PROBABILISTIC MODEL.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Article
- Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer.Frontiers in oncology · 2023Article
- Analysis of Deep Ultraviolet Fluorescence Images for Intraoperative Breast Tumor Margin Assessment.Proceedings of SPIE--the International Society for Optical EngineeringArticle
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Authors and funding
9 authors.
Funding
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Abstract
Microscopy with ultraviolet surface excitation (MUSE) is increasingly studied for intraoperative assessment of tumor margins during breast-conserving surgery to reduce the re-excision rate. Here we report a two-step classification approach using texture analysis of MUSE images to automate the margin detection. A study dataset consisting of MUSE images from 66 human breast tissues was constructed for model training and validation. Features extracted using six texture analysis methods were investigated for tissue characterization, and a support vector machine was trained for binary classification of image patches within a full image based on selected feature subsets. A weighted majority voting strategy classified a sample as tumor or normal. Using the eight most predictive features ranked by the maximum relevance minimum redundancy and Laplacian scores methods has achieved a sample classification accuracy of 92.4% and 93.0%, respectively. Local binary pattern alone has achieved an accuracy of 90.3%.
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Registered trials
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