使用全场光学相干断层扫描技术对人卵巢组织进行分类和分析。
Classification and analysis of human ovarian tissue using full field optical coherence tomography.
作者信息
Nandy Sreyankar, Sanders Melinda, Zhu Quing
机构信息
Department of Biomedical Engineering, Washington University in St. Louis, USA.
University of Connecticut Health Center, Division of Pathology, USA.
出版信息
Biomed Opt Express. 2016 Nov 17;7(12):5182-5187. doi: 10.1364/BOE.7.005182. eCollection 2016 Dec 1.
Abstract
In this study, a full field optical coherence tomography (FFOCT) system was used to analyze and classify normal and malignant human ovarian tissue. 14 ovarian tissue samples (7 normal, 7 malignant) were imaged with the FFOCT system and five features were extracted by analyzing the normalized image histogram from 56 FFOCT images, based on the differences in the morphology of the normal and malignant tissue samples. A generalized linear model (GLM) classifier was trained using 36 images, and sensitivity of 95.3% and specificity of 91.1% was obtained. 20 images were used to test the model, and a sensitivity of 91.6% and specificity of 87.7% was obtained.
摘要
在本研究中，使用全场光学相干断层扫描（FFOCT）系统对正常和恶性人卵巢组织进行分析和分类。用FFOCT系统对14个卵巢组织样本（7个正常样本，7个恶性样本）进行成像，并基于正常和恶性组织样本形态学上的差异，通过分析来自56幅FFOCT图像的归一化图像直方图提取了五个特征。使用36幅图像训练广义线性模型（GLM）分类器，获得了95.3%的灵敏度和91.1%的特异性。使用20幅图像对该模型进行测试，获得了91.6%的灵敏度和87.7%的特异性。
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