SAMBA: A Trainable Segmentation Web-App with Smart Labelling

TL;DR

SAMBA is a web-based, trainable segmentation tool for materials science images that combines Meta's SAM model with a random forest classifier, enabling accessible, high-quality, and generalizable image segmentation without local software installation.

Contribution

It introduces a user-friendly, cloud-based segmentation platform that integrates SAM with a random forest classifier for improved accuracy and accessibility in materials science applications.

Findings

High-quality label suggestions via SAM improve segmentation accuracy.

Web-based platform enables easy access without local installation.

Combines deep learning and traditional classifiers for robust segmentation.

Abstract

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.