VecHeart: Holistic Four-Chamber Cardiac Anatomy Modeling via Hybrid VecSets
- URL: http://arxiv.org/abs/2604.19403v1
- Date: Tue, 21 Apr 2026 12:27:38 GMT
- Title: VecHeart: Holistic Four-Chamber Cardiac Anatomy Modeling via Hybrid VecSets
- Abstract summary: VecHeart is a unified framework for holistic reconstruction and generation of four-chamber cardiac structures.<n>VecHeart seamlessly extends to 3D+t dynamic mesh sequence generation, demonstrating exceptional versatility.
- Score: 41.275254607029595
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Accurate cardiac anatomy modeling requires the model to be able to handle intricate interrelations among structures. In this paper, we propose VecHeart, a unified framework for holistic reconstruction and generation of four-chamber cardiac structures. To overcome the limitations of current feed-forward implicit methods, specifically their restriction to single-object modeling and their neglect of inter-part correlations, we introduce Hybrid Part Transformer, which leverages part-specific learnable queries and interleaved attention to capture complex inter-chamber dependencies. Furthermore, we propose Anatomical Completion Masking and Modality Alignment strategies, enabling the model to infer complete four-chamber structures from partial, sparse, or noisy observations, even when certain anatomical parts are entirely missing. VecHeart also seamlessly extends to 3D+t dynamic mesh sequence generation, demonstrating exceptional versatility. Experiments show that our method achieves state-of-the-art performance, maintaining high-fidelity reconstruction across diverse challenging scenarios. Code will be released.
Related papers
- AnaDiffusion: Anatomically CompositionalLatent Diffusion for Controllable 3D Brain MRI Generation [58.56402940400457]
3D brain MRI generation has made significant advances in medical imaging, simulation, and controllable anatomical analysis.<n>We introduce AnaDiffusion, an anatomically compositional latent diffusion framework that factorizes the generation process into distinct, anatomically meaningful regions.<n>AnaDiffusion produces both explicit part assets and a globally coherent volume, enabling controllable part editing.
arXiv Detail & Related papers (2026-08-24T09:16:42Z) - AuricularWorld: Hierarchical Action-Guided World Modeling for Fine-Grained Auricular Structure Segmentation from CT Scans [4.139833999494916]
We propose a world-model-based segmentation framework that enables iterative anatomical reasoning.<n>We show that the proposed framework consistently improves segmentation accuracy and reduces HD95 by more than 43% for small, irregular, and overlapping auricular structures in CT.
arXiv Detail & Related papers (2026-07-30T16:38:30Z) - From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation [40.52388056968461]
We present a semi-automatic pipeline that converts CT-based segmentations into simulation-ready cardiac meshes.<n>A Chamfer-distance morphing strategy deforms a high-quality template toward each segmented heart, matching individual chambers while preserving topology.<n>The pipeline is validated on 58 healthy cardiac CT scans, including all cardiac chambers and proximal vessel segments.
arXiv Detail & Related papers (2026-06-29T09:07:05Z) - PrIntMesh: Precise Intersection Surfaces for 3D Organ Mesh Reconstruction [37.06867816829073]
PrIntMesh is a template-based, topology-preserving framework that reconstructs organs as unified systems.<n>We demonstrate its effectiveness on the heart, hippocampus, and lungs.
arXiv Detail & Related papers (2025-11-20T09:50:56Z) - CardioComposer: Flexible and Compositional Anatomical Structure Generation with Disentangled Geometric Guidance [0.6312872285702812]
We propose a framework for guiding unconditional diffusion models of human anatomy using interpretable ellipsoidal primitives embedded in 3D space.<n>Our method involves the selection of certain tissues within multi-tissue segmentation maps, upon which we apply geometric moment losses to guide the reverse diffusion process.
arXiv Detail & Related papers (2025-09-08T23:08:23Z) - Surgical Triplet Recognition via Diffusion Model [59.50938852117371]
Surgical triplet recognition is an essential building block to enable next-generation context-aware operating rooms.
We propose Difft, a new generative framework for surgical triplet recognition employing the diffusion model.
Experiments on the CholecT45 and CholecT50 datasets show the superiority of the proposed method in achieving a new state-of-the-art performance for surgical triplet recognition.
arXiv Detail & Related papers (2024-06-19T04:43:41Z) - ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction [59.971808117043366]
ReShapeIT represents an anatomical structure with an implicit template field shared within the same category.
It ensures the implicit template field generates valid templates by strengthening the constraint of the correspondence between the instance shape and the template shape.
A template Interaction Module is introduced to reconstruct unseen shapes by interacting the valid template shapes with the instance-wise latent codes.
arXiv Detail & Related papers (2023-12-11T07:09:32Z) - Shape of my heart: Cardiac models through learned signed distance functions [33.29148402516714]
In this work, the cardiac shape is reconstructed by means of three-dimensional deep signed distance functions with Lipschitz regularity.
For this purpose, the shapes of cardiac MRI reconstructions are learned to model the spatial relation of multiple chambers.
We demonstrate that this approach is also capable of reconstructing anatomical models from partial data, such as point clouds from a single ventricle.
arXiv Detail & Related papers (2023-08-31T09:02:53Z) - Pairwise-Constrained Implicit Functions for 3D Human Heart Modelling [60.56741715207466]
We introduce a pairwise-constrained SDF approach that models the heart as a set of interdependent SDFs.<n>Our method significantly improves inner structure accuracy over single-SDF, UDF-based, voxel-based, and segmentation-based reconstructions.
arXiv Detail & Related papers (2023-07-16T10:07:15Z) - A Generative Shape Compositional Framework to Synthesise Populations of
Virtual Chimaeras [52.33206865588584]
We introduce a generative shape model for complex anatomical structures, learnable from datasets of unpaired datasets.
We build virtual chimaeras from databases of whole-heart shape assemblies that each contribute samples for heart substructures.
Our approach significantly outperforms a PCA-based shape model (trained with complete data) in terms of generalisability and specificity.
arXiv Detail & Related papers (2022-10-04T13:36:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.