CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification
📰 ArXiv cs.AI
Learn to develop a quantitative phenotyping framework for interpretable abdominal CT disease classification using segmentation-derived phenotypes
Action Steps
- Develop a quantitative phenotyping framework using CT image-derived phenotypes
- Generate multi-organ segmentations using TotalSegmentator
- Derive quantitative phenotypes from segmentation results
- Train a disease classification model using the derived phenotypes
- Evaluate the model on independent datasets to ensure generalizability
Who Needs to Know This
This study benefits radiologists and medical imaging researchers who want to improve disease classification accuracy and interpretability in abdominal CT scans. The team can use this framework to develop more accurate and reliable diagnostic tools.
Key Insight
💡 Segmentation-derived quantitative phenotypes can improve the accuracy and interpretability of abdominal CT disease classification
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📚 Develop interpretable abdominal CT disease classification models using segmentation-derived quantitative phenotypes #CTIDP #MedicalImaging
Key Takeaways
Learn to develop a quantitative phenotyping framework for interpretable abdominal CT disease classification using segmentation-derived phenotypes
Full Article
Title: CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification
Abstract:
arXiv:2605.09002v1 Announce Type: cross Abstract: In this retrospective multi-institutional study, a quantitative phenotyping framework, CT-IDP (CT Image-Derived Phenotypes) was developed on the MERLIN abdominal CT benchmark (training, validation, and test sets- 15,175, 5,018, and 5,082 studies, respectively) and externally evaluated on two independent dataset: Duke-Abdomen (2,000) and AMOS (1,107). Multi-organ segmentations were generated with TotalSegmentator and used to derive over 900 organ
Abstract:
arXiv:2605.09002v1 Announce Type: cross Abstract: In this retrospective multi-institutional study, a quantitative phenotyping framework, CT-IDP (CT Image-Derived Phenotypes) was developed on the MERLIN abdominal CT benchmark (training, validation, and test sets- 15,175, 5,018, and 5,082 studies, respectively) and externally evaluated on two independent dataset: Duke-Abdomen (2,000) and AMOS (1,107). Multi-organ segmentations were generated with TotalSegmentator and used to derive over 900 organ
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