6 citations · 8 across the 9 of their papers we have counts for
9 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…
Final Report, Center for Computer-Integrated Computer-Integrated Surgical Systems and Technology, NSF ERC Cooperative Agreement EEC9731748, Volume 1
Russell H. Taylor, Gregory D. Hager, Ralph Etienne-Cummings. Eric Grimson +2
In the last ten years, medical robotics has moved from the margins to the mainstream. Since the Engineering Research Center for Computer-Integrated Surgical Systems and Technology…
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…
LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification
Reuben Dorent, Roya Khajavi, Tagwa Idris +24
Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentati…
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…
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…