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.

翻译：我们介绍了ReXGroundingCT，这是首个公开可用的数据集，将自由文本发现与胸部CT扫描中的像素级3D分割关联起来。该数据集包含3,142例非增强胸部CT扫描，并配有来自CT-RATE的标准化放射学报告。构建过程遵循结构化的三阶段流程。首先，使用GPT-4从最初以土耳其语撰写并机器翻译为英语的报告中提取并标准化发现、描述符和元数据。其次，GPT-4o-mini将每个发现分类到肺部和胸膜异常的分层本体中。第三，为所有CT体积生成了3D标注：训练集由获得委员会认证的放射科医生进行质量保证，验证集和测试集则由获得委员会认证的放射科医生完全标注。此外，还创建了一个补充的思维链数据集，使用GPT-4o和源自器官分割模型的定位坐标，为在CT体积内定位发现提供逐步的分层解剖推理。ReXGroundingCT包含16,301个标注实体，分布在8,028个文本到3D分割对中，涵盖了来自3,142例非增强CT扫描的多种放射学模式。约79%的发现是局灶性异常，21%是非局灶性异常。该数据集包括一个包含50个病例的公开验证集和一个包含100个病例的私有测试集，两者均由获得委员会认证的放射科医生标注。该数据集为在CT成像中实现自由文本发现分割和基于证据的放射学报告生成奠定了基础。模型在私有测试集上的性能托管在公共排行榜上，网址为https://rexrank.ai/ReXGroundingCT。数据集可在https://huggingface.co/datasets/rajpurkarlab/ReXGroundingCT获取。