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

• URL: http://arxiv.org/abs/2403.08974v3
• Date: Tue, 18 Jun 2024 23:32:30 GMT
• Title: $TrIND$: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields
• Authors: Ashish Sinha, Ghassan Hamarneh,
• Abstract summary: Anatomical trees play a central role in clinical diagnosis and treatment planning. Traditional methods for representing tree structures exhibit drawbacks in terms of resolution, flexibility, and efficiency. We propose a novel approach, $TrIND$, for representing anatomical trees using implicit neural representations.
• Score: 17.943355593568242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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: \texttt{\url{https://github.com/sinashish/TreeDiffusion}}.

Related papers

• Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network [1.324564545341267]
  We present a novel approach for a robust semi-automatic vessel tracing algorithm on human fundus images by an instance segmentation neural network (InSegNN) InSegNN separates and labels different vascular trees individually and therefore enable tracing each tree throughout its branching. We have demonstrated tracing individual vessel trees from fundus images, and simultaneously retain the vessel hierarchy information.
  arXiv  Detail & Related papers  (2024-02-15T16:25:28Z)
• Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields [56.69755544814834]
  We present evidence suggesting that depthwise convolutional kernels are effectively replicating the biological receptive fields observed in the mammalian retina. We propose a scheme that draws inspiration from the biological receptive fields.
  arXiv  Detail & Related papers  (2024-01-18T18:06:22Z)
• Evaluating the point cloud of individual trees generated from images based on Neural Radiance fields (NeRF) method [2.4199520195547986]
  In this study, based on tree images collected by various cameras, the Neural Radiance Fields (NeRF) method was used for individual tree reconstruction. The results show that the NeRF method performs well in individual tree 3D reconstruction, as it has higher successful reconstruction rate, better reconstruction in the canopy area. The accuracy of tree structural parameters extracted from the photogrammetric point cloud is still higher than those of derived from the NeRF point cloud.
  arXiv  Detail & Related papers  (2023-12-06T09:13:34Z)
• ARTree: A Deep Autoregressive Model for Phylogenetic Inference [6.935130578959931]
  We propose a deep autoregressive model for phylogenetic inference based on graph neural networks (GNNs) We demonstrate the effectiveness and efficiency of our method on a benchmark of challenging real data tree topology density estimation and variational phylogenetic inference problems.
  arXiv  Detail & Related papers  (2023-10-14T10:26:03Z)
• PhyloGFN: Phylogenetic inference with generative flow networks [57.104166650526416]
  We introduce the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and phylogenetic inference. Because GFlowNets are well-suited for sampling complex structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets.
  arXiv  Detail & Related papers  (2023-10-12T23:46:08Z)
• A Hybrid Approach to Full-Scale Reconstruction of Renal Arterial Network [5.953404851562665]
  We propose a hybrid framework to build subject-specific models of the renal vascular network. We use semi-automated segmentation of large arteries and estimation of cortex area from a micro-CT scan as a starting point. Our results show a statistical correspondence between the reconstructed data and existing anatomical data obtained from a rat kidney.
  arXiv  Detail & Related papers  (2023-03-03T10:39:25Z)
• Tree Reconstruction using Topology Optimisation [0.685316573653194]
  We present a general method for extracting the branch structure of trees from point cloud data. We discuss the benefits and drawbacks of this novel approach to tree structure reconstruction. Our method generates detailed and accurate tree structures in most cases.
  arXiv  Detail & Related papers  (2022-05-26T07:08:32Z)
• BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification [53.53758990624962]
  We propose a region and structure prior embedded framework named BronchusNet to achieve accurate bronchial analysis. For bronchus segmentation, we propose an adaptive hard region-aware UNet that incorporates multi-level prior guidance of hard pixel-wise samples. For the classification of bronchial branches, we propose a hybrid point-voxel graph learning module.
  arXiv  Detail & Related papers  (2022-05-14T02:32:33Z)
• Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder [50.591267188664666]
  We propose an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data. We then introduce DTAE, a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space.
  arXiv  Detail & Related papers  (2021-02-11T08:48:48Z)
• Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity [79.83903179393164]
  This paper further extends the deep forest idea in several important aspects. We employ a probabilistic tree whose nodes make probabilistic routing decisions, a.k.a., soft routing, rather than hard binary decisions. Experiments on the MNIST dataset demonstrate that our empowered deep forests can achieve better or comparable performance than [1],[3].
  arXiv  Detail & Related papers  (2020-12-29T18:05:05Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)

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.