activity
20172022
most citedTechnical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks

17 citations · 17 across the 5 of their papers we have counts for

collaborators

7 papers

eess.IV2022

Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning

Jeffrey Dominic, Nandita Bhaskhar, Arjun D. Desai +8

Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medic…

eess.IV2022

SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation

Arjun D Desai, Andrew M Schmidt, Elka B Rubin +9

Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and d…

eess.IV2020

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Arjun D. Desai, Francesco Caliva, Claudia Iriondo +26

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthriti…

eess.IV201917 cited

Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks

Arjun D. Desai, Garry E. Gold, Brian A. Hargreaves +1

High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications…

physics.med-ph2018

Circumventing the Curse of Dimensionality in Magnetic Resonance Fingerprinting through a Deep Learning Approach

Marco Barbieri, Leonardo Brizi, Enrico Giampieri +4

MR fingerprinting (MRF) is a rapid growing approach for fast quantitave MRI. A typical drawback of dictionary-based MRF is its explosion in size as a function of the number of reco…

cs.CV2018

Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging

Akshay Chaudhari, Zhongnan Fang, Jin Hyung Lee +2

Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and resea…