Real-time reflectance confocal microscopy: comparison of two-dimensional images and three-dimensional image stacks for detection of cervical precancer
Real-time reflectance confocal microscopy: comparison of two-dimensional images and three-dimensional image stacks for detection of cervical precancer
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DOI：
10.1117/1.2717899
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发表时间：
2007-03-01
影响因子：
3.5
通讯作者：
Richards-Kortum, Rebecca
中科院分区：
文献类型：
--
作者：
Collier, Tom;Guillaud, Martial;Richards-Kortum, Rebecca
共焦显微镜可以提供实时的、二维和三维的细胞形态和组织结构特征的图像，病理学家使用这些图像来检测癌前病变，而不需要切除组织、切片和染色。上皮组织3-D共聚焦图像堆叠在检测异型增生中的应用尚未被探索。我们的目标是从新鲜、未染色的宫颈活检组织的2-D共聚焦反射图像和3-D图像组中提取形态测量和组织结构信息，并比较它们在检测异型增生方面的潜力。取自8名患者的9份活检组织；共聚焦图像在冰醋酸前后以1.5微米的间隔在多个上皮深度处获得。对醋酸后处理后的图像进行细胞核分割；分割后，在组织表面下50微米处拍摄二维图像，并对整个三维图像堆栈进行处理，以提取形态和建筑特征。对数据进行分析，以确定哪些特征对正常和高级别宫颈癌前病变的区分最好。最显著的差异来自从3-D图像堆栈提取的参数。然而，在通过采集深度除以上皮厚度或通过散射系数对2-D特征进行乘法缩放的所有情况下，显著程度等于或大于从3-D图像堆栈提取的可比特征。先前开发的线性判别函数基于核浆比(N/C)和上皮散射系数来区分19个正常组织和高级别宫颈癌前病变样本，被前瞻性地应用于所检查的9个活检组织，以确定其区分正常组织和宫颈上皮内瘤变(CIN)2/3的准确性。对于整个28个活检组织的数据集，使用该判别函数产生的灵敏度和特异度为100%；散射系数提供了比N/C比更好的区分能力。规模化二维图像特征的成功对于在临床上使用共聚焦显微镜检测癌前病变具有重要的意义。获取上皮厚度或散射系数所需的时间比3D图像集少，并且与单独的2D图像相比，几乎不需要额外的工作来获得额外的信息。(C)2007年，光学仪器工程师学会。
Confocal microscopy can provide real-time, 2-D and 3-D images of the cellular morphology and tissue architecture features that pathologists use to detect precancerous lesions without the need for tissue removal, sectioning, and staining. The utility of 3-D confocal image stacks of epithelial tissue for detecting dysplasia has not yet been explored. We aim to extract morphometry and tissue architecture information from 2-D confocal reflectance images and 3-D image stacks from fresh, unstained cervical biopsies and compare their potential for detecting dysplasia. Nine biopsies are obtained from eight patients; confocal images are acquired pre- and postacetic acid at multiple epithelial depths in 1.5 mu m-intervals. Postacetic acid images are processed to segment cell nuclei; after segmentation, 2-D images taken at 50 mu m below the tissue surface, and the entire 3-D image stacks are processed to extract morphological and architectural features. Data are analyzed to determine which features gave the best separation between normal and high-grade cervical precancer. Most significant differences are obtained from parameters extracted from the 3-D image stacks. However, in all cases where the 2-D features were multiplicatively scaled by the depth of acquisition divided by the epithelial thickness or scaled by the scattering coefficient, the significance level is equal to or greater than the comparable feature extracted from the 3-D image stacks. A linear discriminant function previously developed to separate 19 samples of normal tissue and high-grade cervical precancer based on the nuclear-to-cytoplasm (N/C) ratio and epithelial scattering coefficient is prospectively applied to the nine biopsies examined to determine the accuracy with which it could separate normal tissue from cervical intra epithelial neoplasia (CIN) 2/3. For the entire data set of 28 biopsies, a sensitivity and specificity of 100% is produced using this discriminant function; the scattering coefficient provides more discriminative capacity than the N/C ratio. The success of the scaled 2-D image features has important implications for using confocal microscopy to detect precancer in the clinic. Acquisition of the epithelial thickness or scattering coefficient requires less time than 3-D image sets and little additional effort is required to gain the added information compared to 2-D images alone. (C) 2007 Society of Photo-Optical Instrumentation Engineers.