Belangrijkste concepten
SAM adaptation for volumetric X-ray data-sets enhances segmentation accuracy in challenging imaging scenarios.
Samenvatting
The article proposes combining SAM with FFN for instance segmentation in NDT scenarios. It evaluates SAM's performance on volumetric data-sets and explores techniques to improve segmentation accuracy. The study establishes a foundation for advancements in instance segmentation.
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Introduction
- Non-Destructive Testing (NDT) involves segmenting large-scale components using X-ray CT.
- Instance segmentation assigns unique identifiers to entities in a data-set.
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Segment Anything Model (SAM)
- SAM is an advanced instance segmentation model based on the vision transformer architecture.
- SAM supports various prompts and generates multiple output masks for each input prompt.
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Combination with Tile-based FFN
- Evaluates SAM's applicability for segmenting volumetric NDT data-sets.
- Utilizes Flood Filling Networks (FFN) to segment objects of arbitrary size based on tiles.
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Contributions
- Evaluation of SAM on NDT data-sets.
- Implementation of techniques to combine image-based SAM with volumetric data-sets.
- Extension of SAM for objects of arbitrary size through tile-based approaches.
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Methods
- Methodology and experimental setup using different tile sizes and post-processing techniques.
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Results
- Evaluation of SAM's segmentation quality in NDT slice data-sets, showcasing challenges and improvements.
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Discussion
- Addressing limitations and potential sources of error in the proposed method.
Statistieken
Our research indicates the potential of combining SAM with FFN for volumetric instance segmentation tasks, particularly in NDT scenarios.
The Segment Anything Model (SAM) stands out for its high quality, robustness, and minimal user input required.