Speaker
Description
Segmentation is the assigning of a semantic class to every pixel in an image, and is a prerequisite for downstream analysis like phase quantifcation, morphological characterization etc. 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 random forest is trained to map from image features to user drawn labels. Meta's recent Segment Anything Model (SAM) is a promptable, open-source macroscopic object detection model that maps an image embedding - generated by a large transformer-based autoencoder - and prompt (click, bounding-box, mask) to a segmentation via a lightweight decoder network. Decoder inference can be run in real-time in the browser, allowing for object segmentation suggestions as the mouse cursor is moved.
SAMBA (Segment Anything Model Based App) is a trainable segmentation tool that uses the SAM model for fast, high-quality label suggestions and random forests for robust, generalizable segmentations. Image embeddings are generated serverside and supplied to the user where clientside decoder inference can take place. It is accessible in the browser (https://www.sambasegment.com/), without the need to download any external dependencies.
