Bellybutton: Accessible and Customizable Deep-Learning Image Segmentation
This addresses the barrier of complex or task-specific tools for researchers needing image segmentation, though it is incremental as it builds on existing convolutional neural network methods.
The paper tackles the problem of making deep-learning image segmentation accessible to non-experts by introducing Bellybutton, an easy-to-use tool that requires no coding and can be trained on a laptop, achieving correct segmentation across varied conditions with as little as one training image in some cases.
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.