mlsanityData to 7 Oct 2026

The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset

arXiv 2405.19595 ↗First posted 30 May 2024cs.CVcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 2 of its 20 references that are in the map. First posted on arXiv 30 May 2024.

Builds on

2 of 20 references.

References · 2

  1. 2018Learning to Reweight Examples for Robust Deep Learningcited by 670
  2. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 597

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 8.

12 papers.

  1. 2025PanTS: The Pancreatic Tumor Segmentation Datasetcited by 15
  2. 2025Revisiting 2D Foundation Models for Scalable 3D Medical Image Classificationcited by 9
  3. 2025Expectation-Maximization as the Engine of Scalable Medical Intelligencecited by 5
  4. 20263DMedAgent: Unified Perception-to-Understanding for 3D Medical Analysiscited by 5
  5. 2026Learning Robust Visual Features in Computed Tomography Enables Efficient Transfer Learning for Clinical Taskscited by 2
  6. 2025Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoostcited by 0
  7. 2026Beyond Calibration: Confounding Pathology Limits Foundation Model Specificity in Abdominal Trauma CTcited by 0
  8. 20263DLAND: 3D Lesion Abdominal Anomaly Localization Datasetcited by 0
  9. 2026Benchmarking CNN-based Models against Transformer-based Models for Abdominal Multi-Organ Segmentation on the RATIC Datasetcited by 0
  10. 2026Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CTcited by 0
  11. 2026Deep Learning Estimation of Sex, Age, Height, and Weight from CT-derived Digitally Reconstructed Radiographscited by 0
  12. 2026RadHarmony: Radiological Data Handling in the Era of Agentic AIcited by 0