Automated assessment of breast margins in deep ultraviolet fluorescence images using texture analysis
Automated assessment of breast margins in deep ultraviolet fluorescence images using texture analysis
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DOI：
10.1364/boe.464547
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发表时间：
2022-09-01
影响因子：
3.4
通讯作者：
Yu, B. I. N. G.
中科院分区：
文献类型：
--
作者：
Lu, T. O. N. G. T. O. N. G.;Jorns, Julie M.;Yu, B. I. N. G.
越来越多的研究表明，在保乳手术中，紫外表面激发显微镜（MUSE）用于术中评估肿瘤边缘，以降低再次切除率。在这里，我们报告了一个两步的分类方法，使用纹理分析的MUSE图像自动边缘检测。构建了由来自66个人类乳腺组织的MUSE图像组成的研究数据集，用于模型训练和验证。研究了使用六种纹理分析方法提取的特征用于组织定征，并训练支持向量机用于基于所选特征子集对完整图像内的图像块进行二进制分类。加权多数投票策略将样品分类为肿瘤或正常。使用最大相关性最小冗余和拉普拉斯得分方法排名的八个最具预测性的特征，分别实现了92.4%和93.0%的样本分类准确率。仅局部二值模式就达到了90.3%的准确率。(c)2022 Optica出版集团根据Optica开放获取出版协议的条款
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%.(c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement