Automated assessment of breast margins in deep ultraviolet fluorescence images using texture analysis

Author:

Lu TongtongORCID,Jorns Julie M.1,Ye Dong Hye2,Patton Mollie1,Fisher Renee3,Emmrich Amanda14,Schmidt Taly Gilat,Yen Tina1,Yu BingORCID

Affiliation:

1. Medical College of Wisconsin

2. Marquette University

3. UDG Healthcare

4. DaVita Clinical Research

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%.

Funder

Marquette University

Medical College of Wisconsin

GHR Foundation

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics,Biotechnology

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