Neural Implicit Heart Coordinates: 3D cardiac shape reconstruction from sparse segmentations
Marica Muffoletto, Uxio Hermida, Charl\`ene Mauger, Avan Suinesiaputra, Yiyang Xu, Richard Burns, Lisa Pankewitz, Andrew D McCulloch, Steffen E Petersen, Daniel Rueckert, Alistair A Young

TL;DR
This paper introduces Neural Implicit Heart Coordinates (NIHCs), a standardized implicit coordinate system for accurate, efficient 3D cardiac shape reconstruction from sparse 2D segmentations, enabling high-resolution models with reduced inference time.
Contribution
The work presents NIHCs, a novel anatomical coordinate system that improves cardiac reconstruction accuracy and efficiency from minimal clinical data, addressing limitations of previous neural implicit methods.
Findings
Achieves mean surface errors of ~2.5 mm in diseased and healthy cohorts.
Reduces inference time from over 60 seconds to 5-15 seconds.
Enables anatomically coherent reconstructions even with severe data sparsity.
Abstract
Accurate reconstruction of cardiac anatomy from sparse clinical images remains a major challenge in patient-specific modeling. While neural implicit functions have previously been applied to this task, their application to mapping anatomical consistency across subjects has been limited. In this work, we introduce Neural Implicit Heart Coordinates (NIHCs), a standardized implicit coordinate system, based on universal ventricular coordinates, that provides a common anatomical reference frame for the human heart. Our method predicts NIHCs directly from a limited number of 2D segmentations (sparse acquisition) and subsequently decodes them into dense 3D segmentations and high-resolution meshes at arbitrary output resolution. Trained on a large dataset of 5,000 cardiac meshes, the model achieves high reconstruction accuracy on clinical contours, with mean Euclidean surface errors of…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Medical Imaging and Analysis · 3D Shape Modeling and Analysis
