Accurate and Unbiased Quantitation of Amyloid-β Fluorescence Images Using ImageSURF

Background: Images of amyloid-β pathology characteristic of Alzheimer’s disease are difficult to consistently andaccurately segment, due to diffuse deposit boundaries and imaging variations.Methods: We evaluated the performance of ImageSURF, our open-source ImageJ plugin, which considers a range of imagederivatives to train image classifiers. We compared ImageSURF to standard image thresholding to assess its reproducibility,accuracy and generalizability when used on fluorescence images of amyloid pathology.Results: ImageSURF segments amyloid-β images significantly more faithfully, and with significantly greatergeneralizability, than optimized thresholding.Conclusion: In addition to its superior performance in capturing human evaluations of pathology images, ImageSURF isable to segment image sets of any size in a consistent and unbiased manner, without requiring additional blinding, and canbe retrospectively applied to existing images. The training process yields a classifier file which can be shared assupplemental data, allowing fully open methods and data, and enabling more direct comparisons between different studies.