Localized Protein Quantification of Blood Brain Barrier Vasculature in Brightfield IHC Images

In this paper, we present an objective method for locally quantifying proteins in blood brain barrier (BBB) vasculature using standard immunohistochemistry (IHC) techniques and bright-field microscopy. Images from the hippocampal region at the BBB are acquired using bright-field microscopy and subjected to our immunohistochemistry quantification (IQ) algorithm which is designed to automatically identify and segment microvessels containing the protein glucose transporter 1 (GLUT1). Gabor filtering and k-means clustering are employed to isolate potential vascular structures within cryopsectioned slabs of the hippocampus, which are subsequently subjected to feature extraction followed by classification via decision forest. The false positive rate (FPR) of microvessel classification is characterized using synthetic and non-synthetic IHC image data for image entropies ranging between 3 and 8 bits. The average FPR for synthetic and non-synthetic IHC image data was found to be 5.48% and 5.04%, respectively.

本研究提出一种客观方法，可利用标准免疫组织化学（immunohistochemistry，IHC）技术与明场显微镜，对血脑屏障（blood brain barrier，BBB）脉管系统内的蛋白质进行原位定量分析。研究团队采集了血脑屏障处海马区域的明场显微镜图像，并将其输入自主开发的免疫组织化学定量（immunohistochemistry quantification，IQ）算法——该算法专为自动识别与分割表达葡萄糖转运蛋白1（glucose transporter 1，GLUT1）的微血管而设计。研究采用Gabor滤波（Gabor filtering）与k均值聚类（k-means clustering），对海马冰冻切片标本中的潜在血管结构进行分离；随后对分离得到的结构执行特征提取（feature extraction），并通过决策森林（decision forest）完成分类。本研究针对图像熵介于3至8比特的样本，利用合成与非合成免疫组织化学图像数据，对微血管分类的假阳性率（false positive rate，FPR）进行了性能表征。实验结果显示，合成与非合成免疫组织化学图像数据的平均假阳性率分别为5.48%与5.04%。