bibtext: @InProceedings{ Sin_TrIND_MICCAI2024,
                   author = { Sinha, Ashish and Hamarneh, Ghassan },
                   title = { { TrIND: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15012 },
                   month = {October},
                   pages = { pending },
                }
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. Our code is available \href{https://github.com/sfu-mial/TreeDiffusion}{here}.
title: TrIND: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields
authors: Sinha, Ashish Hamarneh, Ghassan