K2S Challenge: From Undersampled K-Space to Automatic Segmentation

K2S Challenge: From Undersampled K-Space to Automatic Segmentation

Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could...

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Bibliographic Details
Main Authors: Aniket A. Tolpadi, Upasana Bharadwaj, Kenneth T. Gao, Rupsa Bhattacharjee, Felix G. Gassert, Johanna Luitjens, Paula Giesler, Jan Nikolas Morshuis, Paul Fischer, Matthias Hein, Christian F. Baumgartner, Artem Razumov, Dmitry Dylov, Quintin van Lohuizen, Stefan J. Fransen, Xiaoxia Zhang, Radhika Tibrewala, Hector Lise de Moura, Kangning Liu, Marcelo V. W. Zibetti, Ravinder Regatte, Sharmila Majumdar, Valentina Pedoia
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Bioengineering
Subjects:
image reconstruction
segmentation
multi-task learning
magnetic resonance imaging
musculoskeletal
deep learning
Online Access:https://www.mdpi.com/2306-5354/10/2/267

Internet

https://www.mdpi.com/2306-5354/10/2/267

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