Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes
Roland Gruber,
Steffen Rüger,
Thomas Wittenberg
Abstract:We propose a new approach for volumetric instance segmentation in X-ray Computed Tomography (CT) data for Non-Destructive Testing (NDT) by combining the Segment Anything Model (SAM) with tile-based Flood Filling Networks (FFN). Our work evaluates the performance of SAM on volumetric NDT data-sets and demonstrates its effectiveness to segment instances in challenging imaging scenarios. We implemented and evaluated techniques to extend the image-based SAM algorithm for the use with volumetric data-sets, enabling… Show more
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