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Adapting SAM for Volumetric X-Ray Data-sets of Arbitrary Sizes
March 20, 2024, 4:42 a.m. | Roland Gruber, Steffen R\"uger, Thomas Wittenberg
cs.LG updates on arXiv.org arxiv.org
Abstract: Objective: 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. Methods: We implemented and evaluated techniques to extend the image-based SAM algorithm fo the use with volumetric data-sets, enabling the …
abstract arxiv cs.cv cs.lg data flood instance networks performance ray sam segment segment anything segment anything model segmentation testing type work x-ray x-ray computed tomography
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