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Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers

About Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers

¿This book constitutes the proceedings of the 14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023, as well as the Cardiac MRI Reconstruction Challenge, CMRxRecon Challenge. There was a total of 53 submissions to the workshop. The 24 regular workshop papers included in this volume were carefully reviewed and selected from 29 paper submissions. They deal with cardiac segmentation, modelling, strain quantification, registration, statistical shape analysis, and quality control. In addition, 21 papers from the CMRxRecon challenge are included in this volume. They focus on fast CMR image reconstruction and provide a benchmark dataset that enables the broader research community to promote advances in this area of research.

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  • Language:
  • English
  • ISBN:
  • 9783031524479
  • Binding:
  • Paperback
  • Pages:
  • 508
  • Published:
  • February 1, 2024
  • Edition:
  • 24001
  • Dimensions:
  • 155x28x235 mm.
  • Weight:
  • 762 g.
Delivery: 2-4 weeks
Expected delivery: December 15, 2024

Description of Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers

¿This book constitutes the proceedings of the 14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023, as well as the Cardiac MRI Reconstruction Challenge, CMRxRecon Challenge.
There was a total of 53 submissions to the workshop.
The 24 regular workshop papers included in this volume were carefully reviewed and selected from 29 paper submissions. They deal with cardiac segmentation, modelling, strain quantification, registration, statistical shape analysis, and quality control.
In addition, 21 papers from the CMRxRecon challenge are included in this volume. They focus on fast CMR image reconstruction and provide a benchmark dataset that enables the broader research community to promote advances in this area of research.

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