The conversion of raw images into quantifiable data can be a major hurdle in experimental research, and typically involves identifying region(s) of interest, a process known as segmentation. Machine learning tools for image segmentation are often specific to a set of tasks, such as tracking cells, or require substantial compute or coding knowledge to train and use. Here we introduce an easy-to-use (no coding required), image segmentation method, using a 15-layer convolutional neural network that can be trained on a laptop: Bellybutton. The algorithm trains on user-provided segmentation of example images, but, as we show, just one or even a portion of one training image can be sufficient in some cases. We detail the machine learning method and give three use cases where Bellybutton correctly segments images despite substantial lighting, shape, size, focus, and/or structure variation across the regions(s) of interest. Instructions for easy download and use, with further details and the datasets used in this paper are available at pypi.org/project/Bellybuttonseg.
@article{arxiv.2309.00058,
title = {Bellybutton: Accessible and Customizable Deep-Learning Image Segmentation},
author = {Sam Dillavou and Jesse M. Hanlan and Anthony T. Chieco and Hongyi Xiao and Sage Fulco and Kevin T. Turner and Douglas J. Durian},
journal= {arXiv preprint arXiv:2309.00058},
year = {2023}
}