Automated detection and classification of nuclei in PAX5 and H&E-stained tissue sections of follicular lymphoma
Automated detection and classification of nuclei in PAX5 and H&E-stained tissue sections of follicular lymphoma
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DOI：
10.1007/s11760-016-0913-6
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发表时间：
2017-01-01
影响因子：
2.3
通讯作者：
Grammalidis, Nikos
中科院分区：
文献类型：
--
作者：
Dimitropoulos, Kosmas;Barmpoutis, Panagiotis;Grammalidis, Nikos
在本文中，我们提出了一种新的框架，用于检测和分类滤泡性淋巴瘤 (FL) 组织样本中的中心母细胞 (CB)，这些样本用 PAX5 和 H&E 染色剂染色并以 1 m 厚度水平切片。通过采用 PAX5 免疫组织化学，我们促进了细胞核的分割，而使用 H&E 染色使我们能够提取与病理学家在 FL 分级诊断中使用的组织学特征相关的纹理信息。对于 PAX5 染色图像中的细胞核分割，我们首先应用基于图割的能量最小化技术，然后受大规模视觉词汇聚类的启发，我们提出了一种用于分离重叠细胞核的新算法。从 PAX5 染色图像中提取的细胞核形态特征通过贝叶斯网络分类器与 H&E 图像中识别的许多纹理特征相结合，旨在对 FL 分级中使用的病理学家知识进行建模。实验结果已经显示了所提出的方法的巨大潜力，平均 F 分数为 94.56%。
In this paper, we propose a novel framework for the detection and classification of centroblasts (CB) in follicular lymphoma (FL) tissue samples stained with PAX5 and H&E stains and sliced at 1 m thickness level. By employing PAX5 immunohistochemistry, we facilitate the segmentation of nuclei, while the use of H&E stain enables us to extract textural information related to histological characteristics used by pathologists in the diagnosis of FL grading. For the segmentation of nuclei in PAX5-stained images, we initially apply an energy minimization technique based on graph cuts and then we propose a novel algorithm for the separation of overlapped nuclei inspired by the clustering of large-scale visual vocabularies. The morphological characteristics of nuclei extracted from PAX5-stained images are combined with a number of textural characteristics identified in H&E images through a Bayesian network classifier, which aims to model pathologists' knowledge used in FL grading. Experimental results have already shown the great potential of the proposed methodology providing an average F-score of 94.56%.