Images of amyloid-β pathology characteristic of Alzheimer's disease are difficult to consistently and accurately segment, due to diffuse deposit boundaries and imaging variations. We evaluated the performance of ImageSURF, our open-source ImageJ plugin, which considers a range of image derivatives 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. ImageSURF segments amyloid-β images significantly more faithfully, and with significantly greater generalizability, than optimized thresholding. In addition to its superior performance in capturing human evaluations of pathology images, ImageSURF is able to segment image sets of any size in a consistent and unbiased manner, without requiring additional blinding, and can be retrospectively applied to existing images. The training process yields a classifier file which can be shared as supplemental data, allowing fully open methods and data, and enabling more direct comparisons between different studies.