BioVision: an application for the automated image analysis of histological sections.
We describe a computer application, "BioVision", that can be trained to quickly and effectively classify and quantify user definable histological objects (e.g., senile plaques, neurofibrillary tangles) within single or double-labeled immunocytochemically stained sections. For a given image population, BioVision is interactively trained (in Independent User Mode) by an investigator to perform the desired classifications. This training yields a statistical model of the different types of objects occurring in the target image population. The resulting model can then be used (in Automated User Mode) to classify all objects in any image or images from the target population. BioVision simplifies the quantification of complex visual objects and improves inter-rater reliability. The program accomplishes classification in two major stages: pixel classification and blob classification. In pixel classification, each pixel is assigned to one of some number of substance classes, based on its chromatic properties and local context, reflecting basic histological distinctions of interest. In the blob classification phase, the image's pixels are first partitioned into "blobs": maximal connected sets of pixels assigned to the same substance class. Then, based on its size, shape, textural and contextual properties, each blob is assigned to a histological object class. A Bayesian classifier is used in each of the pixel and blob classification stages. We report several tests of BioVision. First, we applied BioVision to classify senile plaques and neurofibrillary tangles in several test cases of Alzheimer's brain immunostained for beta-amyloid and PHF-tau and compared the results to those produced by experienced investigators. BioVision was trained to classify Plaque-type blobs as either plaques or plaque-type nonentities, and tangle-type blobs as either tangles or tangle-type nonentities. BioVision classified the objects with an accuracy comparable to the trained investigator. Next, we applied BioVision to the task of counting all the tangles in hippocampal images from 22 Alzheimer's disease (AD) cases selected to span a broad range of dementia levels from the tissue repository of UC Irvine's Center for the study of Brain Aging and Dementia. The tangle counts produced by BioVision proved to be significantly better predictors of the cases' adjusted MMSE scores than any of tangle load, age at death, post mortem interval or the interval between the last MMSE score and death.
Chubb
Charles
C
Department of Cognitive Sciences, and Institute for Mathematical Behavioral Sciences, University of California, Irvine, USA. cfchubb@uci.edu
Inagaki
Yoshiyuki
Y
Sheu
Phillip
P
Cummings
Brian
B
Wasserman
Andrea
A
Head
Elizabeth
E
Cotman
Carl
C
D000544:Alzheimer Disease / Q000473:pathology*; D000818:Animals; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D016874:Neurofibrillary Tangles / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D058225:Plaque, Amyloid / Q000473:pathology*; D012984:Software