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

arXiv: https://arxiv.org/abs/2507.22030 · HTML: https://mlsanity.com/paper/2507.22030/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography](https://mlsanity.com/paper/2403.17834/) (2024)
- [MAIRA-2: Grounded Radiology Report Generation](https://mlsanity.com/paper/2406.04449/) (2024)
- [BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once](https://mlsanity.com/paper/2405.12971/) (2024)
- [SegVol: Universal and Interactive Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.13385/) (2023)
- [MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine](https://mlsanity.com/paper/2408.02900/) (2024)
- [Large-Vocabulary Segmentation for Medical Images with Text Prompts](https://mlsanity.com/paper/2312.17183/) (2023)
- [RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis](https://mlsanity.com/paper/2404.16754/) (2024)
- [RadGPT: Constructing 3D Image-Text Tumor Datasets](https://mlsanity.com/paper/2501.04678/) (2025)
- [PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology Report Generation](https://mlsanity.com/paper/2411.05085/) (2024)
- [The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography](https://mlsanity.com/paper/2406.05231/) (2024)
- [ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation](https://mlsanity.com/paper/2411.15122/) (2024)
- [Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings](https://mlsanity.com/paper/2103.14572/) (2021)
- [ReXplain: Translating Radiology into Patient-Friendly Video Reports](https://mlsanity.com/paper/2410.00441/) (2024)
- [Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach](https://mlsanity.com/paper/2506.03238/) (2025)

## Built on by (most cited first, 17 of 17)

- [3DMedAgent: Unified Perception-to-Understanding for 3D Medical Analysis](https://mlsanity.com/paper/2602.18064/) (2026)
- [U-VLM: Hierarchical Vision Language Modeling for Report Generation](https://mlsanity.com/paper/2603.00479/) (2026)
- [MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows](https://mlsanity.com/paper/2603.24649/) (2026)
- [LCMem: A Universal Model for Robust Image Memorization Detection](https://mlsanity.com/paper/2512.14421/) (2025)
- [MedReason-R1: Learning to Reason for CT Diagnosis with Reinforcement Learning and Local Zoom](https://mlsanity.com/paper/2510.19626/) (2025)
- [Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space](https://mlsanity.com/paper/2603.13800/) (2026)
- [Enhancing Fine-Grained Spatial Grounding in 3D CT Report Generation via Discriminative Guidance](https://mlsanity.com/paper/2604.10437/) (2026)
- [CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretation](https://mlsanity.com/paper/2604.26288/) (2026)
- [GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained Radiology Representations](https://mlsanity.com/paper/2606.03180/) (2026)
- [RadGame: An AI-Powered Platform for Radiology Education](https://mlsanity.com/paper/2509.13270/) (2025)
- [Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation](https://mlsanity.com/paper/2605.30893/) (2026)
- [Open-Ended CT Volume Segmentation with Weak Supervision from Language](https://mlsanity.com/paper/2607.25860/) (2026)
- [DALE-CT: Depth-Aware 2D Slice Encoders Learn an Anatomical World Model of Chest CT](https://mlsanity.com/paper/2606.07775/) (2026)
- [Decouple and Reason: Anatomically Guided Two-Stage Voxel-Level Grounding of Free-Text Findings in 3D Chest CT](https://mlsanity.com/paper/2607.12602/) (2026)
- [MedPlex: Deep Vision-Language Co-Adaptation for Clinically Grounded Medical Segmentation](https://mlsanity.com/paper/2608.13690/) (2026)
- [Instance-Guided Report Anchoring for Text-Free 3D Abnormality Segmentation in Chest CT](https://mlsanity.com/paper/2609.00447/) (2026)
- [The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging](https://mlsanity.com/paper/2610.13237/) (2026)

## Abstract (arXiv)

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.