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人双核淋巴细胞中微核的自动评分

Automated scoring of micronuclei in binucleated human lymphocytes.

作者信息

Böcker W, Streffer C, Müller W U, Yu C

机构信息

Institut für Medizinische Strahlenbiologie, Universitäts-klinikum Essen, Germany.

出版信息

Int J Radiat Biol. 1996 Nov;70(5):529-37. doi: 10.1080/095530096144734.

Abstract

Manual and automatic scoring of micronuclei (MN) in binucleated human lymphocytes (BNC) were compared after irradiation of whole blood samples. The blood samples were irradiated with X-ray doses (1, 2 or 3 Gy) and stained with Giemsa. The preparation technique was optimized in such a way that acceptable conditions (cell density, contrast) were obtained for both scoring procedures. To estimate the quality of automatic micronucleus detection, two researchers who had different experience in scoring MN (6 months and 5 years) analysed the samples independently from each other. Automatic scoring was carried out with a digital image analysis system and the recognition procedure was divided into two parts. The BNC positions were detected with low microscope magnification (100x), and the recognition of micronuclei within the cytoplasm of the classified BNC was carried out at high magnification (630x). A fuzzy logic classification system as well as two different segmentation steps (preclassification and postclassification) made it possible that about 94% of all automatically recognized BNC were classified correctly). On the other hand, the classification system was optimized in such a way that false positive decisions were minimized (95% of automatically recognized micronuclei were classified correctly). Failure to recognize micronuclei (8.5%-25% false negatives) was mainly due to extremely small micronuclei, poor contrast with respect to the cytoplasm, and aggregation of micronuclei especially at higher doses.

摘要

对全血样本进行辐照后,比较了对双核人类淋巴细胞(BNC)中的微核(MN)进行手动和自动评分的情况。血液样本用X射线剂量(1、2或3 Gy)进行辐照,并用吉姆萨染色。制备技术经过优化,以便为两种评分程序获得可接受的条件(细胞密度、对比度)。为了评估自动微核检测的质量,两名在微核评分方面经验不同(6个月和5年)的研究人员彼此独立地分析样本。使用数字图像分析系统进行自动评分,识别程序分为两部分。在低倍显微镜(100倍)下检测BNC的位置,并在高倍(630倍)下识别分类后的BNC细胞质内的微核。模糊逻辑分类系统以及两个不同的分割步骤(预分类和后分类)使得大约94%的所有自动识别的BNC能够被正确分类。另一方面,分类系统经过优化,以使假阳性判断最小化(95%的自动识别的微核被正确分类)。未能识别微核(8.5%-25%的假阴性)主要是由于微核极小、与细胞质的对比度差以及微核聚集,尤其是在较高剂量时。

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