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The conversion of raw images into quantifiable data can be a major hurdle inexperimental research, and typically involves identifying region(s) ofinterest, a process known as segmentation. Machine learning tools for imagesegmentation are often specific to a set of tasks, such as tracking cells, orrequire substantial compute or coding knowledge to train and use. Here weintroduce an easy-to-use (no coding required), image segmentation method, usinga 15-layer convolutional neural network that can be trained on a laptop:Bellybutton. The algorithm trains on user-provided segmentation of exampleimages, but, as we show, just one or even a portion of one training image canbe sufficient in some cases. We detail the machine learning method and givethree use cases where Bellybutton correctly segments images despite substantiallighting, shape, size, focus, and/or structure variation across the regions(s)of interest. Instructions for easy download and use, with further details andthe datasets used in this paper are available atpypi.org/project/Bellybuttonseg.

Bellybutton: Accessible and Customizable Deep-Learning Image Segmentation