SAMBA: A Trainable Segmentation Web-App with Smart Labelling
Ronan Docherty Isaac Squires Antonis Vamvakeros Samuel J. Cooper
Segmentation is the assigning of a semantic class to every pixel in an image
and is a prerequisite for various statistical analysis tasks in materials
science, like phase quantification, physics simulations or morphological
characterization. The wide range of length scales, imaging techniques and
materials studied in materials science means any segmentation algorithm must
generalise to unseen data and support abstract, user-defined semantic classes.
Trainable segmentation is a popular interactive segmentation paradigm where a
classifier is trained to map from image features to user drawn labels. SAMBA is
a trainable segmentation tool that uses Meta's Segment Anything Model (SAM) for
fast, high-quality label suggestions and a random forest classifier for robust,
generalizable segmentations. It is accessible in the browser
(https://www.sambasegment.com/) without the need to download any external
dependencies. The segmentation backend is run in the cloud, so does not require
the user to have powerful hardware.