1 citations · 1 across the 4 of their papers we have counts for
4 papers
MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging
Leonard Nürnberg, Dennis Bontempi, Suraj Pai +17
Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are…
CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
Murong Xu, Tamaz Amiranashvili, Fernando Navarro +30
Accurate delineation of anatomical structures in volumetric CT scans is crucial for diagnosis and treatment planning. While AI has advanced automated segmentation, current approach…
Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation
Deepa Krishnaswamy, Cosmin Ciausu, Steve Pieper +3
Recent advances in deep learning have led to robust automated tools for segmentation of abdominal computed tomography (CT). Meanwhile, segmentation of magnetic resonance imaging (M…
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
Gowtham Krishnan Murugesan, Diana McCrumb, Rahul Soni +8
AI in Medical Imaging project aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by developing nnU-Net models and providing AI-assisted segmentations fo…