Adapting SAM for Volumetric X-Ray Data-sets of Arbitrary Sizes
This addresses instance segmentation for non-destructive testing applications, but appears incremental as it adapts existing models to a new data type.
The authors tackled volumetric instance segmentation in X-ray CT data for non-destructive testing by combining the Segment Anything Model (SAM) with tile-based Flood Filling Networks (FFN), demonstrating its effectiveness for segmenting large objects in challenging imaging scenarios.
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.