mlsanityData to 14 Oct 2026

TrIND: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields

arXiv 2403.08974 ↗First posted 13 Mar 2024cs.CV cs.AIcited by 4

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Anatomical trees play a central role in clinical diagnosis and treatment planning. However, accurately representing anatomical trees is challenging due to their varying and complex topology and geometry. Traditional methods for representing tree structures, captured using medical imaging, while invaluable for visualizing vascular and bronchial networks, exhibit drawbacks in terms of limited resolution, flexibility, and efficiency. Recently, implicit neural representations (INRs) have emerged as a powerful tool for representing shapes accurately and efficiently. We propose a novel approach, TrIND, for representing anatomical trees using INR, while also capturing the distribution of a set of trees via denoising diffusion in the space of INRs. We accurately capture the intricate geometries and topologies of anatomical trees at any desired resolution. Through extensive qualitative and quantitative evaluation, we demonstrate high-fidelity tree reconstruction with arbitrary resolution yet compact storage, and versatility across anatomical sites and tree complexities. The code is available at: texttturlhttps://github.com/sinashish/TreeDiffusion.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 17 of its 42 references that are in the map. First posted on arXiv 13 Mar 2024.

Builds on

17 of 42 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2020Denoising Diffusion Implicit Modelscited by 9,781
  4. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  5. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  6. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  7. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 700
  8. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 458
  9. 2016Volumetric and Multi-View CNNs for Object Classification on 3D Datacited by 240
  10. 2023HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusioncited by 152
  11. 2022Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functionscited by 133
  12. 2022Learning to Learn with Generative Models of Neural Network Checkpointscited by 86
5 more
  1. 2021Subdivision-Based Mesh Convolution Networkscited by 60
  2. 2020IntrA: 3D Intracranial Aneurysm Dataset for Deep Learningcited by 41
  3. 2022Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modelingcited by 16
  4. 2018Blood Vessel Geometry Synthesis using Generative Adversarial Networkscited by 13
  5. 2023VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesiscited by 13

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025VesselGPT: Autoregressive Modeling of Vascular Geometrycited by 5
  2. 2025Hierarchical Part-based Generative Model for Realistic 3D Blood Vesselcited by 4
  3. 2026High-Fidelity Medical Shape Generation via Skeletal Latent Diffusioncited by 1
  4. 2025Recursive Variational Autoencoders for 3D Blood Vessel Generative Modelingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Semantically Consistent Discrete Diffusion for 3D Biological Graph Modeling
  2. 2026Autoregressive Frontier Expansion: Growing Trees with Graph Machine Learning
  3. 20243D Vessel Graph Generation Using Denoising Diffusion
  4. 2025Integrating Anatomical Priors into a Causal Diffusion Model
  5. 2024Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
  6. 2024Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors
  7. 2026TubeMLLM: A Foundation Model for Topology Knowledge Exploration in Vessel-like Anatomy
  8. 2025StructDiff: Structure-aware Diffusion Model for 3D Fine-grained Medical Image Synthesis