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arXiv AI

Local2Mesh: Spatially Localized Contour-to-Mesh for Left Ventricular Reconstruction from Sparse 2D Cardiac MRI

Local2Mesh is a spatially localized contour‑to‑mesh framework that reconstructs 3‑D left ventricular geometry from sparse 2‑D cardiac MRI contours without requiring 3‑D mesh annotations. It corrects inter‑slice misalignment through geometry‑aware alignment and routes contour features to template vertices via a plane‑aware Local Router, then jointly uses local and global features to deform a template mesh. Experiments on M&Ms‑2 and ACDC datasets show superior geometric reconstruction, functional estimation, and disease classification compared to existing methods, with strong cross‑dataset generalization.

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arXiv Computer Vision
Sep 25

Anatomy-Aligned Surface Field Learning for Myocardial Reconstruction from Sparse Short-Axis Cine MRI

The paper presents an anatomy‑aligned surface learning framework for reconstructing patient‑specific 4D myocardial surfaces from sparsely sampled short‑axis cine MRI. By parameterizing epicardial and endocardial surfaces on a shared circumferential‑longitudinal UV domain, the method transforms irregular 3D reconstruction into structured coordinate‑field completion, enabling explicit correspondence across subjects and cardiac phases. Experiments on three public datasets show the approach outperforms mesh‑based and implicit methods, achieving Chamfer distances around 2.6–2.9 mm and preserving ventricular function with small errors in volume and ejection fraction.

By Xiaohan Yuan, Xuan Yang, Qingya Li, Yangang Wang, Lei Li
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arXiv AI
Jun 12

Transformer-Guided Graph Attention for Direct Cardiac Mesh Reconstruction: A Structural Digital Twin Framework

arXiv:2606. 13188v1 Announce Type: cross Abstract: Building patient-specific cardiac models sits at the heart of precision cardiology, yet getting those models into clinical use keeps running into the same wall: mesh generation is slow, messy, and frustrating.

By Abhishek H S, Akash Ganamukhi, Abhimanyu Suresh, Aditya G Hiremath, Prasad B Honnavalli, Adithya Balasubramanyam
llmscomputer-visionbenchmarks
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arXiv AI
Jul 7

From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation

arXiv:2607. 02564v1 Announce Type: cross Abstract: Computational models of the human heart are widely used to study electromechanical and fluid-dynamical cardiac function and to support applications such as in silico clinical trials.

By Francesco Fabbri, Martino Andrea Scarpolini, Paolo Ciancarella, Francesco Tudisco, Roberto Verzicco, Alessandro Ricci, Francesco Viola
diffusioncomputer-vision
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arXiv Computer Vision
4d ago

PPCAR-Net: Projection-Refined Parametric 3D Coronary Artery Reconstruction from Sparse X-ray Angiographic Views

PPCAR-Net is a neural network that reconstructs 3‑D coronary artery centrelines and radii from a sparse set of X‑ray angiographic views. It predicts a branch‑structured centreline-and‑radius representation directly, avoiding explicit point matching or triangulation. The method refines geometry and radius estimates using projection‑guided refiners and achieves real‑time performance with strong centreline accuracy, especially for the right coronary artery.

By Yu Ren, Hwee Kuan Lee, Tat-Jen Cham, Jonathan Yap, Khung Keong Yeo
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arXiv Computer Vision
Sep 14

SV-Cine: Diagnosis-Conditioned Segmentation of Single Ventricle Physiology via Generative Data Augmentation

The paper introduces SV-Cine, a cardiac MRI segmentation framework tailored for single ventricle physiology (SVP). It combines a generative data augmentation pipeline that creates synthetic 3D cardiac meshes and MRI, with a diagnosis-conditioned adaptation of the CineMA foundation model that uses patient-level diagnostic information to improve segmentation. Evaluations on an internal cohort show high Dice scores for left and right ventricles, outperforming nnU-Net, and demonstrate that incorporating diagnosis priors can adapt a pretrained model to specialized SVP tasks.

By Lila Cunge, Yuehong Liu, Hang Xu, Thomas Coudert, Pierangelo Renella, J Paul Finn, William Hsu, Kim-Lien Nguyen
computer-vision
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arXiv Computer Vision
Aug 27

Steer the Sampling, Not the Kernel Grid: Geometry-Guided Sampling Operator for Volumetric Segmentation

The paper introduces a geometry‑guided sampling operator that directs feature sampling rather than altering convolution kernels in 3D encoder‑decoder networks. By predicting local orientations and bounded step sizes, the operator samples symmetrically around each voxel, generating compact geometric and boundary cues that improve fine‑structure segmentation. Replacing stride‑1 and stride‑2 operations in a 3D U‑Net yields consistent gains on BraTS, MSD Hepatic Vessel, and TDSC‑ABUS datasets, with better boundary metrics and fewer parameters, and the operator can be integrated into other backbones without architectural changes.

By Sizhe Wang, Himashi Peiris, Zhaolin Chen
computer-vision
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