Characterization of ovarian tissue based on quantitative analysis of photoacoustic microscopy images.
Characterization of ovarian tissue based on quantitative analysis of photoacoustic microscopy images.
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DOI：
10.1364/boe.4.002763
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发表时间：
2013-12
影响因子：
3.4
通讯作者：
Tianheng Wang;Yi Yang;Umar S. Alqasemi;P. Kumavor;Xiaohong Wang;M. Sanders;M. Brewer;Quing Zhu
中科院分区：
文献类型：
--
作者：
Tianheng Wang;Yi Yang;Umar S. Alqasemi;P. Kumavor;Xiaohong Wang;M. Sanders;M. Brewer;Quing Zhu
在本文中，使用光学分辨率光声显微镜（OR-PAM）系统对具有恶性和良性特征的人类卵巢组织进行离体成像。通过比较 PAM 图像的形态学，探讨 PAM 区分恶性和正常卵巢组织的可行性。根据观察正常和恶性卵巢组织PAM图像在微血管特征和分布上的差异，从PAM图像中定量提取7个特征，并使用Logistic模型将卵巢分类为正常或恶性。研究了来自 18 个卵巢的 106 张 PAM 图像。使用57张图像训练七参数Logistic模型，特异性达到92.1%，敏感性达到89.5%；随后测试了 49 张图像，特异性为 81.3%，敏感性为 88.2%。这些初步结果证明了我们的 PAM 系统在绘制微血管网络以及表征卵巢组织方面的可行性，并且对于协助外科医生在微创手术期间对卵巢组织进行体内评估非常有价值。
In this paper, human ovarian tissue with malignant and benign features was imaged ex vivo using an optical-resolution photoacoustic microscopy (OR-PAM) system. The feasibility of PAM to differentiate malignant from normal ovarian tissues was explored by comparing the PAM images morphologically. Based on the observed differences between PAM images of normal and malignant ovarian tissues in microvasculature features and distributions, seven features were quantitatively extracted from the PAM images, and a logistic model was used to classify ovaries as normal or malignant. 106 PAM images from 18 ovaries were studied. 57 images were used to train the seven-parameter logistic model, and a specificity of 92.1% and a sensitivity of 89.5% were achieved; 49 images were then tested, and a specificity of 81.3% and a sensitivity of 88.2% were achieved. These preliminary results demonstrate the feasibility of our PAM system in mapping microvasculature networks as well as characterizing the ovarian tissue, and could be extremely valuable in assisting surgeons for in vivo evaluation of ovarian tissue during minimally invasive surgery.