The RSNA Abdominal Traumatic Injury CT (RATIC) dataset is the largest publicly available collection of adult abdominal CT studies annotated for traumatic injuries. This dataset includes 4,274 studies from 23 institutions across 14 countries. The dataset is freely available for non-commercial use via Kaggle at https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection. Created for the RSNA 2023 Abdominal Trauma Detection competition, the dataset encourages the development of advanced machine learning models for detecting abdominal injuries on CT scans. The dataset encompasses detection and classification of traumatic injuries across multiple organs, including the liver, spleen, kidneys, bowel, and mesentery. Annotations were created by expert radiologists from the American Society of Emergency Radiology (ASER) and Society of Abdominal Radiology (SAR). The dataset is annotated at multiple levels, including the presence of injuries in three solid organs with injury grading, image-level annotations for active extravasations and bowel injury, and voxelwise segmentations of each of the potentially injured organs. With the release of this dataset, we hope to facilitate research and development in machine learning and abdominal trauma that can lead to improved patient care and outcomes.
@article{arxiv.2405.19595,
title = {The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset},
author = {Jeffrey D. Rudie and Hui-Ming Lin and Robyn L. Ball and Sabeena Jalal and Luciano M. Prevedello and Savvas Nicolaou and Brett S. Marinelli and Adam E. Flanders and Kirti Magudia and George Shih and Melissa A. Davis and John Mongan and Peter D. Chang and Ferco H. Berger and Sebastiaan Hermans and Meng Law and Tyler Richards and Jan-Peter Grunz and Andreas Steven Kunz and Shobhit Mathur and Sandro Galea-Soler and Andrew D. Chung and Saif Afat and Chin-Chi Kuo and Layal Aweidah and Ana Villanueva Campos and Arjuna Somasundaram and Felipe Antonio Sanchez Tijmes and Attaporn Jantarangkoon and Leonardo Kayat Bittencourt and Michael Brassil and Ayoub El Hajjami and Hakan Dogan and Muris Becircic and Agrahara G. Bharatkumar and Eduardo Moreno Júdice de Mattos Farina and Dataset Curator Group and Dataset Contributor Group and Dataset Annotator Group and Errol Colak},
journal= {arXiv preprint arXiv:2405.19595},
year = {2024}
}