Bellybutton: Accessible and Customizable Deep-Learning Image Segmentation

TL;DR

Bellybutton is an accessible, customizable deep-learning image segmentation tool that requires no coding, can be trained on a laptop with minimal data, and effectively handles diverse imaging conditions.

Contribution

It introduces a user-friendly, no-code CNN-based segmentation method that can be trained with minimal data and is suitable for diverse experimental imaging scenarios.

Findings

Successfully segments images with varying lighting, shape, and focus.

Requires minimal training data, sometimes just a single image.

Operates efficiently on a standard laptop.

Abstract

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.