Robust Cell Segmentation for Histological Images of Glioblastoma.
Robust Cell Segmentation for Histological Images of Glioblastoma.
复制标题
胶质母细胞瘤组织学图像的稳健细胞分割。
DOI：
10.1109/isbi.2016.7493444
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Wang,Fusheng
中科院分区：
文献类型：
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作者：
Kong,Jun;Zhang,Pengyue;Liang,Yanhui;Teodoro,George;Brat,DanielJ;Wang,Fusheng
胶质母细胞瘤是一种预后均较差的恶性脑肿瘤。对基底膜细胞的定量分析是提取潜在的组织学疾病特征以与分子基础和临床结果相关的重要途径。作为先决条件，需要一个健壮且准确的细胞分割。在这篇文章中，我们提出了一种自动细胞分割方法，可以很好地解决GBM组织标本中常见的重叠细胞的分割问题。该方法首先检测具有种子连通性、距离约束、图像边缘图和基于形状的投票图像的细胞。通过识别种子进行初始化，使用改进的变分水平集方法对单元边界进行变形，该方法可以处理聚集的单元。我们在40幅带有人类注释的基底膜组织图像上测试了我们的方法。验证结果表明，我们的细胞分割方法是有前景的，代表了定量癌症研究的进步。
Glioblastoma (GBM) is a malignant brain tumor with uniformly dismal prognosis. Quantitative analysis of GBM cells is an important avenue to extract latent histologic disease signatures to correlate with molecular underpinnings and clinical outcomes. As a prerequisite, a robust and accurate cell segmentation is required. In this paper, we present an automated cell segmentation method that can satisfactorily address segmentation of overlapped cells commonly seen in GBM histology specimens. This method first detects cells with seed connectivity, distance constraints, image edge map, and a shape-based voting image. Initialized by identified seeds, cell boundaries are deformed with an improved variational level set method that can handle clumped cells. We test our method on 40 histological images of GBM with human annotations. The validation results suggest that our cell segmentation method is promising and represents an advance in quantitative cancer research.