Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation.
Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation.
复制标题
使用基于自适应的细胞分割的卵泡淋巴瘤分级的计算机辅助检测用于卵泡淋巴瘤分级的检测。
DOI：
10.1109/tbme.2010.2055058
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发表时间：
2010-10
期刊：
影响因子：
--
通讯作者：
Gurcan MN
中科院分区：
文献类型：
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作者：
Sertel O;Lozanski G;Shana'ah A;Gurcan MN
滤泡性淋巴瘤是西方国家最常见的淋巴系统恶性肿瘤之一。FL具有可变的临床过程，并且FL患者的重要临床治疗决策基于组织学分级，其通过在来自H& E染色的组织切片的十个标准显微镜高功率视野中手动计数称为中心母细胞（CB）的大恶性细胞来完成。这种方法是繁琐和主观的;因此，即使由专家病理学家使用，也存在相当大的阅片者间和阅片者内的差异。在这项研究中，我们提出了一个计算机辅助检测系统，用于从H& E染色的FL组织样本中自动识别CB细胞。所提出的系统使用一个unitone转换，以获得具有最高对比度的单通道图像。从由于H& E染色而具有双峰分布的所得图像，生成细胞似然图像。最后，一个两步CB检测程序。在第一步中，我们根据大小和形状减少明显的非CB细胞。在第二步中，通过学习和利用非CB细胞的纹理分布来进一步改进CB检测。我们评估了所提出的方法从10个不同的组织样本中提取的100个感兴趣区域的图像，并获得了有前途的80.7%的检测精度。
Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL has a variable clinical course and important clinical treatment decisions for FL patients are based on histological grading, which is done by manual counting of large malignant cells called centroblasts (CB) in ten standard microscopic high power fields from H&E-stained tissue sections. This method is tedious and subjective; as a result suffers from considerable inter- and intra-reader variability even when used by expert pathologists. In this study, we present a computer-aided detection system for automated identification of CB cells from H&E-stained FL tissue samples. The proposed system uses a unitone conversion to obtain a single channel image that has the highest contrast. From the resulting image, which has a bi-modal distribution due to the H&E-stain, a cell-likelihood image is generated. Finally, a two-step CB detection procedure is applied. In the first step, we reduce evident non-CB cells based on size and shape. In the second step CB detection is further refined by learning and utilizing the texture distribution of non-CB cells. We evaluated the proposed approach on 100 region of interest images extracted from ten distinct tissue samples and obtained a promising 80.7% detection accuracy.