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.

本研究提出了一种基于标准免疫组化（immunohistochemistry, IHC）技术与明场显微镜（bright-field microscopy）的客观方法，可实现血脑屏障（blood brain barrier, BBB）脉管系统内蛋白质的原位定量分析。研究人员通过明场显微镜采集血脑屏障海马区的图像，并将其输入至本研究设计的免疫组化量化（immunohistochemistry quantification, IQ）算法中，该算法可自动识别并分割表达葡萄糖转运蛋白1（glucose transporter 1, GLUT1）的微血管。本研究采用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脉管结构进行分离，随后对其提取特征并通过决策森林（decision forest）完成分类。本研究针对图像熵（image entropies）介于3至8比特的合成与非合成免疫组化图像数据，对微血管分类的假阳性率（false positive rate, FPR）进行了表征分析。研究发现，合成与非合成免疫组化图像数据的平均假阳性率分别为5.48%与5.04%。