Adapting SAM for Volumetric X-Ray Data-sets of Arbitrary Sizes
Roland Gruber Steffen Rüger Thomas Wittenberg
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 segmentation of three-dimensional objects using FFN's spatially
adaptability. The tile-based approach for SAM leverages FFN's capabilities to
segment objects of any size. We also explore the use of dense prompts to guide
SAM in combining segmented tiles for improved segmentation accuracy. Results:
Our research indicates the potential of combining SAM with FFN for volumetric
instance segmentation tasks, particularly in NDT scenarios and segmenting large
entities and objects. Conclusion: While acknowledging remaining limitations,
our study provides insights and establishes a foundation for advancements in
instance segmentation in NDT scenarios.