Janowczyk Andrew, Chandran Sharat, Singh Rajendra, Sasaroli Dimitra, Coukos George, Feldman Michael D, Madabhushi Anant
Dept of Computer Science & Engineering, Indian Institute of Technology, Bombay.
Med Image Comput Comput Assist Interv. 2009;12(Pt 1):230-8. doi: 10.1007/978-3-642-04268-3_29.
Research has shown that tumor vascular markers (TVMs) may serve as potential OCa biomarkers for prognosis prediction. One such TVM is ESM-1, which can be visualized by staining ovarian Tissue Microarrays (TMA) with an antibody to ESM-1. The ability to quickly and quantitatively estimate vascular stained regions may yield an image based metric linked to disease survival and outcome. Automated segmentation of the vascular stained regions on the TMAs, however, is hindered by the presence of spuriously stained false positive regions. In this paper, we present a general, robust and efficient unsupervised segmentation algorithm, termed Hierarchical Normalized Cuts (HNCut), and show its application in precisely quantifying the presence and extent of a TVM on OCa TMAs. The strength of HNCut is in the use of a hierarchically represented data structure that bridges the mean shift (MS) and the normalized cuts (NCut) algorithms. This allows HNCut to efficiently traverse a pyramid of the input image at various color resolutions, efficiently and accurately segmenting the object class of interest (in this case ESM-1 vascular stained regions) by simply annotating half a dozen pixels belonging to the target class. Quantitative and qualitative analysis of our results, using 100 pathologist annotated samples across multiple studies, prove the superiority of our method (sensitivity 81%, Positive predictive value (PPV), 80%) versus a popular supervised learning technique, Probabilistic Boosting Trees (sensitivity, PPV of 76% and 66%).
研究表明，肿瘤血管标志物（TVM）可能作为预测卵巢癌预后的潜在生物标志物。其中一种TVM是内皮细胞特异性分子-1（ESM-1），可通过用抗ESM-1抗体对卵巢组织微阵列（TMA）进行染色来可视化。快速且定量估计血管染色区域的能力可能产生与疾病生存和结局相关的基于图像的指标。然而，TMA上血管染色区域的自动分割受到虚假染色的假阳性区域的阻碍。在本文中，我们提出了一种通用、稳健且高效的无监督分割算法，称为分层归一化切割（HNCut），并展示了其在精确量化卵巢癌TMA上TVM的存在和范围方面的应用。HNCut的优势在于使用分层表示的数据结构，该结构桥接了均值漂移（MS）和归一化切割（NCut）算法。这使得HNCut能够在各种颜色分辨率下有效地遍历输入图像的金字塔，通过简单地标注属于目标类别的半打像素，高效且准确地分割感兴趣的对象类别（在这种情况下为ESM-1血管染色区域）。使用来自多个研究的100个病理学家标注的样本对我们的结果进行定量和定性分析，证明了我们的方法（敏感性81%，阳性预测值（PPV）80%）优于一种流行的监督学习技术——概率提升树（敏感性、PPV分别为76%和66%）。
