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计算机图像分析自动测量宫颈上皮内瘤变的上皮分化和分类。

Automatic measurement of epithelium differentiation and classification of cervical intraneoplasia by computerized image analysis.

机构信息

Cabinet de Pathologie, 34 Rue Ducouedic, 75014 Paris, France.

出版信息

Diagn Pathol. 2010 Jan 22;5:7. doi: 10.1186/1746-1596-5-7.

Abstract

BACKGROUND

The feasibility of evaluating an objective grading of cervical intraneoplasia lesions (CIN) is attempted using an automatic computerized system able to measure several valuable parameters with special reference to epithelium differentiation.

METHODS

4 groups of 10 images each were selected at random from 68 consensus images coming from 80 archival cervical biopsies, normal (n = 10), CIN 1 (n = 10), CIN 2 (n = 10), CIN 3 (n = 10). Representative images of lesions were captured from the microscopic slides and were analyzed using mathematical morphology, with special reference toVoronoï tessellation and Delaunay triangulation. Epithelium surface, nuclear and cytoplasm area, triangle edge and area, total and upper nuclear index were precisely measured in each lesion, and discriminant coefficients were calculated therewith. A dilation/erosion coefficient was automatically defined using triangle edge length and nuclear radius in order to measure the epithelium ratio of differentiation. A histogram ratio was also automatically established between total nuclei and upper nuclei on top of differentiated epithelium. With the latter two ratios added to the nucleo-cytoplasmic ratio, a cervical score able to classify CIN is proposed.

RESULTS

There is a quasi-linear increase of mean cervical score values between normal epithelium and CIN 3: (27) for normal epithelium, (51) for CIN 1, (78) for CIN 2 and (100) for CIN 3, with significant differences (P < 0.05).

CONCLUSION

Our results highlight the possibility of applying a cervical score for the automatic grading of CIN lesions and thereby assisting the pathologist for improvement of grading. The automatic measure of epithelium differentiation ratio appears to be a new interesting parameter in computerized image analysis of cervical lesions.

摘要

背景

尝试使用能够测量上皮分化等多个有价值参数的自动计算机系统评估宫颈内瘤变病变(CIN)的客观分级的可行性。

方法

从 80 例存档宫颈活检的 68 个共识图像中随机选择了 4 组各 10 个图像,分别为正常(n = 10)、CIN1(n = 10)、CIN2(n = 10)、CIN3(n = 10)。从显微镜载玻片上捕获病变的代表性图像,并使用数学形态学进行分析,特别参考 Voronoi 镶嵌和 Delaunay 三角剖分。在每个病变中精确测量上皮表面、核和细胞质面积、三角形边缘和面积、总核指数和上核指数,并计算相应的判别系数。通过三角形边缘长度和核半径自动定义扩张/侵蚀系数,以测量上皮分化比。还自动在上皮分化的总核和上核之间建立核质比的直方图比。将后两个比与核质比相加,提出了一种能够对 CIN 进行分类的宫颈评分。

结果

正常上皮与 CIN3 之间的平均宫颈评分值呈准线性增加:(27)为正常上皮,(51)为 CIN1,(78)为 CIN2,(100)为 CIN3,差异有统计学意义(P<0.05)。

结论

我们的结果强调了应用宫颈评分自动分级 CIN 病变的可能性,并由此协助病理学家提高分级准确性。上皮分化比的自动测量似乎是宫颈病变计算机图像分析中的一个新的有趣参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d487/2819044/b904de5cb0ae/1746-1596-5-7-1.jpg

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