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

arXiv: https://arxiv.org/abs/2403.12066 · HTML: https://mlsanity.com/paper/2403.12066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [A Survey on Instance Segmentation: State of the art](https://mlsanity.com/paper/2007.00047/) (2020)
- [Bridging 2D and 3D Segmentation Networks for Computation Efficient Volumetric Medical Image Segmentation: An Empirical Study of 2.5D Solutions](https://mlsanity.com/paper/2010.06163/) (2020)
- [Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping](https://mlsanity.com/paper/2401.08787/) (2024)
- [An annotated instance segmentation XXL-CT data-set from a historic airplane](https://mlsanity.com/paper/2212.08639/) (2022)
- [Instance Segmentation XXL-CT Challenge of a Historic Airplane](https://mlsanity.com/paper/2402.02928/) (2024)

## Abstract (arXiv)

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.