Automated assessment of breast margins in deep ultraviolet fluorescence images using texture analysis. Biomed Opt Express 2022 Sep 01;13(9):5015-5034
Date
10/04/2022Pubmed ID
36187258Pubmed Central ID
PMC9484420DOI
10.1364/BOE.464547Scopus ID
2-s2.0-85138797028 (requires institutional sign-in at Scopus site) 4 CitationsAbstract
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%.
Author List
Lu T, Jorns JM, Ye DH, Patton M, Fisher R, Emmrich A, Schmidt TG, Yen T, Yu BAuthors
Taly Gilat-Schmidt PhD Associate Professor of Biomedical Engineering in the Biomedical Engineering department at Marquette UniversityJulie M. Jorns MD Professor in the Pathology department at Medical College of Wisconsin
Tina W F Yen MD, MS Professor in the Surgery department at Medical College of Wisconsin
Bing Yu PH.D. Assistant Professor of Biomedical Engineering in the Biomedical Engineering department at Marquette University
