ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports

• URL: http://arxiv.org/abs/2507.22030v1
• Date: Tue, 29 Jul 2025 17:27:15 GMT
• Title: ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
• Authors: Mohammed Baharoon, Luyang Luo, Michael Moritz, Abhinav Kumar, Sung Eun Kim, Xiaoman Zhang, Miao Zhu, Mahmoud Hussain Alabbad, Maha Sbayel Alhazmi, Neel P. Mistry, Kent Ryan Kleinschmidt, Brady Chrisler, Sathvik Suryadevara, Sri Sai Dinesh Jaliparthi, Noah Michael Prudlo, Mark David Marino, Jeremy Palacio, Rithvik Akula, Hong-Yu Zhou, Ibrahim Ethem Hamamci, Scott J. Adams, Hassan Rayhan AlOmaish, Pranav Rajpurkar,
• Abstract summary: We present ReXGroundingCT, the first publicly available dataset to link free-text radiology findings with pixel-level segmentations in 3D chest CT scans.<n>The dataset comprises 3142, non-contrast chest CT scans paired with standardized radiology reports from the CT-RATE dataset.<n>A total of 8,028 findings across 16,301 entities were annotated, with quality control performed by board-certified radiologists.
• Score: 12.58423214857364
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present ReXGroundingCT, the first publicly available dataset to link free-text radiology findings with pixel-level segmentations in 3D chest CT scans that is manually annotated. While prior datasets have relied on structured labels or predefined categories, ReXGroundingCT captures the full expressiveness of clinical language represented in free text and grounds it to spatially localized 3D segmentation annotations in volumetric imaging. This addresses a critical gap in medical AI: the ability to connect complex, descriptive text, such as "3 mm nodule in the left lower lobe", to its precise anatomical location in three-dimensional space, a capability essential for grounded radiology report generation systems. The dataset comprises 3,142 non-contrast chest CT scans paired with standardized radiology reports from the CT-RATE dataset. Using a systematic three-stage pipeline, GPT-4 was used to extract positive lung and pleural findings, which were then manually segmented by expert annotators. A total of 8,028 findings across 16,301 entities were annotated, with quality control performed by board-certified radiologists. Approximately 79% of findings are focal abnormalities, while 21% are non-focal. The training set includes up to three representative segmentations per finding, while the validation and test sets contain exhaustive labels for each finding entity. ReXGroundingCT establishes a new benchmark for developing and evaluating sentence-level grounding and free-text medical segmentation models in chest CT. The dataset can be accessed at https://huggingface.co/datasets/rajpurkarlab/ReXGroundingCT.

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