PrIntMesh: Precise Intersection Surfaces for 3D Organ Mesh Reconstruction

• URL: http://arxiv.org/abs/2511.16186v1
• Date: Thu, 20 Nov 2025 09:50:56 GMT
• Title: PrIntMesh: Precise Intersection Surfaces for 3D Organ Mesh Reconstruction
• Authors: Deniz Sayin Mercadier, Hieu Le, Yihong Chen, Jiancheng Yang, Udaranga Wickramasinghe, Pascal Fua,
• Abstract summary: 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.
• Score: 37.06867816829073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human organs are composed of interconnected substructures whose geometry and spatial relationships constrain one another. Yet, most deep-learning approaches treat these parts independently, producing anatomically implausible reconstructions. We introduce PrIntMesh, a template-based, topology-preserving framework that reconstructs organs as unified systems. Starting from a connected template, PrIntMesh jointly deforms all substructures to match patient-specific anatomy, while explicitly preserving internal boundaries and enforcing smooth, artifact-free surfaces. We demonstrate its effectiveness on the heart, hippocampus, and lungs, achieving high geometric accuracy, correct topology, and robust performance even with limited or noisy training data. Compared to voxel- and surface-based methods, PrIntMesh better reconstructs shared interfaces, maintains structural consistency, and provides a data-efficient solution suitable for clinical use.

Related papers

• AGENet: Adaptive Edge-aware Geodesic Distance Learning for Few-Shot Medical Image Segmentation [0.0]
  AGENet is a novel framework that incorporates spatial relationships through edge-aware geodesic distance learning.<n>Our key insight is that medical structures follow predictable geometric patterns that can guide prototype extraction.<n>Our method reduces boundary errors compared to existing approaches while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-11-11T09:56:35Z)
• 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)
• Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction? [61.48804987263701]
  We propose a suite of anatomy-aware evaluation metrics to assess structural completeness across anatomical structures.<n> CARE incorporates structural penalties during training to encourage anatomical preservation of significant structures.<n> CARE substantially improves structural completeness in CT reconstructions, achieving up to +32% improvement for large organs, +22% for small organs, +40% for intestines, and +36% for vessels.
  arXiv  Detail & Related papers  (2025-06-02T17:07:10Z)
• JADE: Joint-aware Latent Diffusion for 3D Human Generative Modeling [62.77347895550087]
  We introduce JADE, a generative framework that learns the variations of human shapes with fined-grained control.<n>Our key insight is a joint-aware latent representation that decomposes human bodies into skeleton structures.<n>To generate coherent and plausible human shapes under our proposed decomposition, we also present a cascaded pipeline.
  arXiv  Detail & Related papers  (2024-12-29T14:18:35Z)
• 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)
• 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)
• A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction [1.8047694351309207]
  We propose a novel deep-learning-based approach that directly predicts whole heart surface meshes from volumetric CT and MR image data. Our method demonstrated promising performance of generating high-resolution and high-quality whole heart reconstructions.
  arXiv  Detail & Related papers  (2021-02-16T00:39:43Z)

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.