Miao Qin, Derbas Justin, Eid Aya, Subramanian Hariharan, Backman Vadim
Biomedical Engineering Department, Northwestern University, Evanston, IL 60208, USA.
NanoCytomics LLC, 1801 Maple Avenue, Evanston, IL 60201, USA.
Biomed Res Int. 2016;2016:6090912. doi: 10.1155/2016/6090912. Epub 2016 Jan 19.
Partial wave spectroscopy (PWS) enables quantification of the statistical properties of cell structures at the nanoscale, which has been used to identify patients harboring premalignant tumors by interrogating easily accessible sites distant from location of the lesion. Due to its high sensitivity, cells that are well preserved need to be selected from the smear images for further analysis. To date, such cell selection has been done manually. This is time-consuming, is labor-intensive, is vulnerable to bias, and has considerable inter- and intraoperator variability. In this study, we developed a classification scheme to identify and remove the corrupted cells or debris that are of no diagnostic value from raw smear images. The slide of smear sample is digitized by acquiring and stitching low-magnification transmission. Objects are then extracted from these images through segmentation algorithms. A training-set is created by manually classifying objects as suitable or unsuitable. A feature-set is created by quantifying a large number of features for each object. The training-set and feature-set are used to train a selection algorithm using Support Vector Machine (SVM) classifiers. We show that the selection algorithm achieves an error rate of 93% with a sensitivity of 95%.
分波谱学（PWS）能够对纳米尺度下细胞结构的统计特性进行量化，该技术已被用于通过检测远离病变部位的易于获取的位点来识别患有癌前肿瘤的患者。由于其高灵敏度，需要从涂片图像中选择保存良好的细胞进行进一步分析。迄今为止，这种细胞选择工作一直是人工完成的。这既耗时、劳动强度大，又容易产生偏差，而且在操作员之间和操作员内部都存在相当大的变异性。在本研究中，我们开发了一种分类方案，以从原始涂片图像中识别并去除没有诊断价值的受损细胞或碎片。通过采集和拼接低倍透射图像将涂片样本玻片数字化。然后通过分割算法从这些图像中提取目标。通过人工将目标分类为合适或不合适来创建一个训练集。通过为每个目标量化大量特征来创建一个特征集。使用支持向量机（SVM）分类器，利用训练集和特征集来训练一种选择算法。我们表明，该选择算法的错误率为93%，灵敏度为95%。
