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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2507.22030 |
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| author | Baharoon, Mohammed Luo, Luyang Moritz, Michael Kumar, Abhinav Kim, Sung Eun Zhang, Xiaoman Zhu, Miao Alabbad, Mahmoud Hussain Alhazmi, Maha Sbayel Mistry, Neel P. Bijnens, Lucas Kleinschmidt, Kent Ryan Chrisler, Brady Suryadevara, Sathvik Jaliparthi, Sri Sai Dinesh Prudlo, Noah Michael Marino, Mark David Palacio, Jeremy Akula, Rithvik Zhou, Di Zhou, Hong-Yu Hamamci, Ibrahim Ethem Adams, Scott J. AlOmaish, Hassan Rayhan Rajpurkar, Pranav |
| author_facet | Baharoon, Mohammed Luo, Luyang Moritz, Michael Kumar, Abhinav Kim, Sung Eun Zhang, Xiaoman Zhu, Miao Alabbad, Mahmoud Hussain Alhazmi, Maha Sbayel Mistry, Neel P. Bijnens, Lucas Kleinschmidt, Kent Ryan Chrisler, Brady Suryadevara, Sathvik Jaliparthi, Sri Sai Dinesh Prudlo, Noah Michael Marino, Mark David Palacio, Jeremy Akula, Rithvik Zhou, Di Zhou, Hong-Yu Hamamci, Ibrahim Ethem Adams, Scott J. AlOmaish, Hassan Rayhan Rajpurkar, Pranav |
| contents | We introduce ReXGroundingCT, the first publicly available dataset linking free-text findings to pixel-level 3D segmentations in chest CT scans. The dataset includes 3,142 non-contrast chest CT scans paired with standardized radiology reports from CT-RATE. Construction followed a structured three-stage pipeline. First, GPT-4 was used to extract and standardize findings, descriptors, and metadata from reports originally written in Turkish and machine-translated into English. Second, GPT-4o-mini categorized each finding into a hierarchical ontology of lung and pleural abnormalities. Third, 3D annotations were produced for all CT volumes: the training set was quality-assured by board-certified radiologists, and the validation and test sets were fully annotated by board-certified radiologists. Additionally, a complementary chain-of-thought dataset was created to provide step-by-step hierarchical anatomical reasoning for localizing findings within the CT volume, using GPT-4o and localization coordinates derived from organ segmentation models. ReXGroundingCT contains 16,301 annotated entities across 8,028 text-to-3D-segmentation pairs, covering diverse radiological patterns from 3,142 non-contrast CT scans. About 79% of findings are focal abnormalities and 21% are non-focal. The dataset includes a public validation set of 50 cases and a private test set of 100 cases, both annotated by board-certified radiologists. The dataset establishes a foundation for enabling free-text finding segmentation and grounded radiology report generation in CT imaging. Model performance on the private test set is hosted on a public leaderboard at https://rexrank.ai/ReXGroundingCT. The dataset is available at https://huggingface.co/datasets/rajpurkarlab/ReXGroundingCT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports Baharoon, Mohammed Luo, Luyang Moritz, Michael Kumar, Abhinav Kim, Sung Eun Zhang, Xiaoman Zhu, Miao Alabbad, Mahmoud Hussain Alhazmi, Maha Sbayel Mistry, Neel P. Bijnens, Lucas Kleinschmidt, Kent Ryan Chrisler, Brady Suryadevara, Sathvik Jaliparthi, Sri Sai Dinesh Prudlo, Noah Michael Marino, Mark David Palacio, Jeremy Akula, Rithvik Zhou, Di Zhou, Hong-Yu Hamamci, Ibrahim Ethem Adams, Scott J. AlOmaish, Hassan Rayhan Rajpurkar, Pranav Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition We introduce ReXGroundingCT, the first publicly available dataset linking free-text findings to pixel-level 3D segmentations in chest CT scans. The dataset includes 3,142 non-contrast chest CT scans paired with standardized radiology reports from CT-RATE. Construction followed a structured three-stage pipeline. First, GPT-4 was used to extract and standardize findings, descriptors, and metadata from reports originally written in Turkish and machine-translated into English. Second, GPT-4o-mini categorized each finding into a hierarchical ontology of lung and pleural abnormalities. Third, 3D annotations were produced for all CT volumes: the training set was quality-assured by board-certified radiologists, and the validation and test sets were fully annotated by board-certified radiologists. Additionally, a complementary chain-of-thought dataset was created to provide step-by-step hierarchical anatomical reasoning for localizing findings within the CT volume, using GPT-4o and localization coordinates derived from organ segmentation models. ReXGroundingCT contains 16,301 annotated entities across 8,028 text-to-3D-segmentation pairs, covering diverse radiological patterns from 3,142 non-contrast CT scans. About 79% of findings are focal abnormalities and 21% are non-focal. The dataset includes a public validation set of 50 cases and a private test set of 100 cases, both annotated by board-certified radiologists. The dataset establishes a foundation for enabling free-text finding segmentation and grounded radiology report generation in CT imaging. Model performance on the private test set is hosted on a public leaderboard at https://rexrank.ai/ReXGroundingCT. The dataset is available at https://huggingface.co/datasets/rajpurkarlab/ReXGroundingCT. |
| title | ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.22030 |