Summary of Ctarr: a Fast and Robust Method For Identifying Anatomical Regions on Ct Images Via Atlas Registration, by Thomas Buddenkotte et al.
CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration
by Thomas Buddenkotte, Roland Opfer, Julia Krüger, Alessa Hering, Mireia Crispin-Ortuzar
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel generic method for CT Anatomical Region Recognition (CTARR) is introduced, serving as a pre-processing step for deep learning-based CT image analysis pipelines. This approach automatically identifies the relevant anatomical region and removes non-relevant parts, reducing computational burden and errors during inference. Applications include image segmentation, classification, and registration. The method uses atlas registration and demonstrates robustness on six public datasets, preserving foreground voxels in 97.45-100% of cases while taking only fractions of a second to compute. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers analyze medical images is being developed. This approach helps reduce the time it takes for computers to look at large medical images and focus on specific areas that are important for diagnosis or treatment. It’s like using a magnifying glass to zoom in on what’s really important. This method can be used for different tasks, such as identifying specific organs or tissues, classifying images based on their contents, and aligning images taken from different angles or times. |
Keywords
» Artificial intelligence » Classification » Deep learning » Image segmentation » Inference
