Neural Implicit Heart Coordinates: 3D cardiac shape reconstruction from sparse segmentations

• URL: http://arxiv.org/abs/2512.19316v2
• Date: Tue, 23 Dec 2025 09:25:34 GMT
• Title: Neural Implicit Heart Coordinates: 3D cardiac shape reconstruction from sparse segmentations
• Authors: Marica Muffoletto, Uxio Hermida, Charlène Mauger, Avan Suinesiaputra, Yiyang Xu, Richard Burns, Lisa Pankewitz, Andrew D McCulloch, Steffen E Petersen, Daniel Rueckert, Alistair A Young,
• Abstract summary: We introduce Neural Implicit Heart Coordinates (NIHCs), a standardized implicit coordinate system, based on universal ventricular coordinates.<n>Our method predicts NIHCs directly from a limited number of 2D segmentations (sparse acquisition) and decodes them into dense 3D segmentations and high-resolution meshes.<n>Trained on a large dataset of 5,000 cardiac meshes, the model achieves anatomically high reconstruction accuracy on clinical contours.
• Score: 14.200667111973729
• License: http://creativecommons.org/licenses/by/4.0/
• 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 2.51$\pm$0.33 mm in a diseased cohort (n=4549) and 2.3$\pm$0.36 mm in a healthy cohort (n=5576). The NIHC representation enables anatomically coherent reconstruction even under severe slice sparsity and segmentation noise, faithfully recovering complex structures such as the valve planes. Compared with traditional pipelines, inference time is reduced from over 60 s to 5-15 s. These results demonstrate that NIHCs constitute a robust and efficient anatomical representation for patient-specific 3D cardiac reconstruction from minimal input data.

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