UCSF Renal Mass CT Dataset

This dataset contains the training data for the Machine learning for Optimal detection of iNflammatory cells in the KidnEY or MONKEY challenge. The MONKEY challenge focuses on the automated detection and classification of inflammatory cells, specifically monocytes and lymphocytes, in kidney transplant biopsies using Periodic acid-Schiff (PAS) stained whole-slide images (WSI). It contains 80 WSI, collected from 4 different pathology institutes, with annotated regions of interest. For each WSI up to 3 different PAS scans and one IHC slide scan are available. This dataset and challenge support the development of AI models that can aid in the diagnostic process, reduce pathologists’ workload, and improve patient outcomes in renal transplantation.

The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) dataset is a public, clinical, multimodal brain MRI dataset consisting of 560 brain MRIs from 412 patients with expert annotations of 5136 brain metastases. Data consists of registered and skull stripped T1 post-contrast, T1 pre-contrast, FLAIR and subtraction (T1 pre-contrast - T1 post-contrast) images and voxelwise segmentations of enhancing brain metastases in NifTI format.

There are multiple treatment options for bunions. Procedures range from minimally invasive to invasive surgical options. Depending on the severitiy of the hallux valgus treament could be an implant designed for joint preservation, pinning, fixation or implant for more severe cases. Utilizing CT imaging can help in 3D modeling on many forms of bunion diseases which helps in the creating and testing of new product models.

Blunt force abdominal trauma is among the most common types of traumatic injury, with the most frequent cause being motor vehicle accidents. Abdominal trauma may result in damage and internal bleeding of the internal organs, including the liver, spleen, kidneys, and bowel. Detection and classification of injuries are key to effective treatment and favorable outcomes. A large proportion of patients with abdominal trauma require urgent surgery. Abdominal trauma often cannot be diagnosed clinically by physical exam, patient symptoms, or laboratory tests. Prompt diagnosis of abdominal trauma using medical imaging is thus critical to patient care. AI tools that assist and expedite diagnosis of abdominal trauma have the potential to substantially improve patient care and health outcomes in the emergency setting. To create the ground truth dataset, RSNA collected imaging data sourced from 23 sites in 14 countries on six continents, including more than 4,000 CT exams with various abdomina[...]