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Simulating Organogenesis: Algorithms for the Image-Based Determination of Displacement Fields

Published: 17 February 2015 Publication History

Abstract

Recent advances in imaging technology now provide us with 3D images of developing organs. These can be used to extract 3D geometries for simulations of organ development. To solve models on growing domains, the displacement fields between consecutive image frames need to be determined. Here we develop and evaluate different landmark-free algorithms for the determination of such displacement fields from image data. In particular, we examine minimal distance, normal distance, diffusion-based, and uniform mapping algorithms and test these algorithms with both synthetic and real data in 2D and 3D. We conclude that in most cases, the normal distance algorithm is the method of choice and wherever it fails, diffusion-based mapping provides a good alternative.

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  1. Simulating Organogenesis: Algorithms for the Image-Based Determination of Displacement Fields

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      Published In

      cover image ACM Transactions on Modeling and Computer Simulation
      ACM Transactions on Modeling and Computer Simulation  Volume 25, Issue 2
      Special Issue on Computational Methods in Systems Biology
      April 2015
      161 pages
      ISSN:1049-3301
      EISSN:1558-1195
      DOI:10.1145/2737798
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 17 February 2015
      Accepted: 01 October 2014
      Revised: 01 July 2014
      Received: 01 January 2014
      Published in TOMACS Volume 25, Issue 2

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      Author Tags

      1. Displacement field
      2. curve mapping
      3. image-based
      4. modelling

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      • SystemsX grants of the Swiss National Fund (SNF)

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      View all
      • (2024)Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production LinesSensors10.3390/s2405161924:5(1619)Online publication date: 1-Mar-2024
      • (2023)Geometric effects position renal vesicles during kidney developmentCell Reports10.1016/j.celrep.2023.11352642:12(113526)Online publication date: Dec-2023
      • (2019)Image-based modeling of kidney branching morphogenesis reveals GDNF-RET based Turing-type mechanism and pattern-modulating WNT11 feedbackNature Communications10.1038/s41467-018-08212-810:1Online publication date: 16-Jan-2019
      • (2018)Simulation of Morphogen and Tissue DynamicsMorphogen Gradients10.1007/978-1-4939-8772-6_13(223-250)Online publication date: 16-Oct-2018
      • (2015)Image-based modelling of organogenesisBriefings in Bioinformatics10.1093/bib/bbv09317:4(616-627)Online publication date: 27-Oct-2015

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