Adapting SAM for Volumetric X-Ray Data-sets of Arbitrary Sizes

arXiv:2403.12066v1 Announce Type: cross
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