The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset

TL;DR

The RATIC dataset is the largest publicly available collection of annotated adult abdominal CT scans for traumatic injuries, designed to advance machine learning models for injury detection and classification.

Contribution

It introduces a comprehensive, multi-institutional dataset with detailed annotations to support research in automated abdominal trauma detection.

Findings

Largest publicly available abdominal trauma CT dataset

Includes multi-level annotations for injuries across organs

Supports development of advanced machine learning models

Abstract

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.