Automated Protein Localization of Blood Brain Barrier Vasculature in Brightfield IHC Images

• Published in: PloS one Vol. 11; no. 2; p. e0148411
• Main Authors: Soans, Rajath E., Lim, Diane C., Keenan, Brendan T., Pack, Allan I., Shackleford, James A.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.02.2016; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Automation; Biology and Life Sciences; Blood; Blood-brain barrier; Blood-Brain Barrier - metabolism; Brain; Brain research; Classification; Cluster Analysis; Clustering; Decision trees; Ecology and Environmental Sciences; Electrical engineering; Engineering and Technology; Entropy; Feature extraction; Filtration; Glucose transporter; Glucose Transporter Type 1 - metabolism; Hippocampus; Hippocampus - metabolism; Image acquisition; Image classification; Image processing; Image Processing, Computer-Assisted; Image segmentation; Immunohistochemistry; Laboratories; Localization; Male; Medicine and Health Sciences; Methods; Mice, Inbred C57BL; Microscopy; Microvessels - metabolism; Morphology; Neurobiology; Neurosciences; Permeability; Physical Sciences; Physiological aspects; Physiology; Protein Transport; Proteins; Proteomics; Reproducibility of Results; Research and Analysis Methods; Rodents; Slabs; Time Factors; Vector quantization; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0148411

In this paper, we present an objective method for localization of 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 segmentation pipeline 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 cryosectioned 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.