1 citations · 3 across the 11 of their papers we have counts for
11 papers
Annotating anatomy and pathology in the National Lung Screening Trial computed tomography images
Deepa Krishnaswamy, Vamsi Thiriveedhi, Suraj Pai +6
Large-scale public medical imaging datasets contribute critically to translational research. When accompanied by rich clinical and multi-omics data, they can stimulate exploratory…
Coronary artery calcification assessment in National Lung Screening Trial CT images (DeepCAC2)
Leonard Nürnberg, Simon Bernatz, Borek Foldyna +3
Coronary artery calcification (CAC) is a strong predictor of cardiovascular risk but remains underutilized in clinical routine thoracic imaging due to the need for dedicated imagin…
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…
In search of truth: Evaluating concordance of AI-based anatomy segmentation models
Lena Giebeler, Deepa Krishnaswamy, David Clunie +9
Purpose AI-based methods for anatomy segmentation can help automate characterization of large imaging datasets. The growing number of similar in functionality models raises the cha…
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…
Rule-based outlier detection of AI-generated anatomy segmentations
Deepa Krishnaswamy, Vamsi Krishna Thiriveedhi, Cosmin Ciausu +4
There is a dire need for medical imaging datasets with accompanying annotations to perform downstream patient analysis. However, it is difficult to manually generate these annotati…
Automatic classification of prostate MR series type using image content and metadata
Deepa Krishnaswamy, Bálint Kovács, Stefan Denner +6
With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial…
Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels
Cosmin Ciausu, Deepa Krishnaswamy, Benjamin Billot +3
Deep learning has shown great promise in the ability to automatically annotate organs in magnetic resonance imaging (MRI) scans, for example, of the brain. However, despite advance…
AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons
Gowtham Krishnan Murugesan, Diana McCrumb, Mariam Aboian +10
The National Cancer Institute (NCI) Image Data Commons (IDC) offers publicly available cancer radiology collections for cloud computing, crucial for developing advanced imaging too…