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<p class="MsoNormal" style="margin-bottom: 0cm; line-height: normal;"><span lang="EN-US" style="font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-GB;">This book constitutes the proceedings of the 16th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2025, held in conjunction with the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, in&#xa0;Daejeon, South Korea on September 27, 2025</span></p><p class="MsoNormal" style="margin-bottom: 0cm; line-height: normal;"><span lang="EN-US" style="font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-GB;">The 26 full workshop papers included in this book were carefully reviewed and selected from 32 submissions. The CMRxRecon Challenge received 22 paper submissions of which 8 are included in this book. They deal with </span><span lang="EN-GB" style="font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-GB;">cardiac segmentation, modelling, motion estimation, statistical shape analysis, and quality control. Deep learning methods were still the predominant approach to performing automated cardiac image analysis. Left atrial image analysis and modelling gained more attention in this workshop, with atrial fibrillation being the common area of interest.</span></p>

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

  • 会议论文集

  • 发表日期：

    2026年

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摘要

This book constitutes the proceedings of the 16th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2025, held in conjunction with the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, in Daejeon, South Korea on September 27, 2025

The 26 full workshop papers included in this book were carefully reviewed and selected from 32 submissions. The CMRxRecon Challenge received 22 paper submissions of which 8 are included in this book. They deal with cardiac segmentation, modelling, motion estimation, statistical shape analysis, and quality control. Deep learning methods were still the predominant approach to performing automated cardiac image analysis. Left atrial image analysis and modelling gained more attention in this workshop, with atrial fibrillation being the common area of interest.