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4th order tensors for multi-fiber resolution and segmentation in white matter

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00354559" target="_blank" >RIV/68407700:21230/20:00354559 - isvavai.cz</a>

  • Nalezeny alternativní kódy

    RIV/00216224:14310/20:00114778

  • Výsledek na webu

    <a href="http://hdl.handle.net/10467/99036" target="_blank" >http://hdl.handle.net/10467/99036</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/3444884.3444892" target="_blank" >10.1145/3444884.3444892</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    4th order tensors for multi-fiber resolution and segmentation in white matter

  • Popis výsledku v původním jazyce

    Since its inception, DTI modality has become an essential tool in the clinical scenario. In principle, it is rooted in the emergence of symmetric positive definite (SPD) second-order tensors modelling the difusion. The inability of DTI to model regions of white matter with fibers crossing/merging leads to the emergence of higher order tensors. In this work, we compare various approaches how to use 4th order tensors to model such regions. There are three different projections of these 3D 4th order tensors to the 2nd order tensors of dimensions either three or six. Two of these projections are consistent in terms of preserving mean diffusivity and isometry. The images of all three projections are SPD, so they belong to a Riemannian symmetric space. Following previous work of the authors, we use the standard k-means segmentation method after dimension reduction with affinity matrix based on reasonable similarity measures, with the goal to compare the various projections to 2nd order tensors. We are using the natural affine and log-Euclidean (LogE) metrics. The segmentation of curved structures and fiber crossing regions is performed under the presence of several levels of Rician noise. The experiments provide evidence that 3D 2nd order reduction works much better than the 6D one, while diagonal components (DC) projections are able to reveal the maximum diffusion direction.

  • Název v anglickém jazyce

    4th order tensors for multi-fiber resolution and segmentation in white matter

  • Popis výsledku anglicky

    Since its inception, DTI modality has become an essential tool in the clinical scenario. In principle, it is rooted in the emergence of symmetric positive definite (SPD) second-order tensors modelling the difusion. The inability of DTI to model regions of white matter with fibers crossing/merging leads to the emergence of higher order tensors. In this work, we compare various approaches how to use 4th order tensors to model such regions. There are three different projections of these 3D 4th order tensors to the 2nd order tensors of dimensions either three or six. Two of these projections are consistent in terms of preserving mean diffusivity and isometry. The images of all three projections are SPD, so they belong to a Riemannian symmetric space. Following previous work of the authors, we use the standard k-means segmentation method after dimension reduction with affinity matrix based on reasonable similarity measures, with the goal to compare the various projections to 2nd order tensors. We are using the natural affine and log-Euclidean (LogE) metrics. The segmentation of curved structures and fiber crossing regions is performed under the presence of several levels of Rician noise. The experiments provide evidence that 3D 2nd order reduction works much better than the 6D one, while diagonal components (DC) projections are able to reveal the maximum diffusion direction.

Klasifikace

  • Druh

    D - Stať ve sborníku

  • CEP obor

  • OECD FORD obor

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Návaznosti výsledku

  • Projekt

    <a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

  • Návaznosti

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Ostatní

  • Rok uplatnění

    2020

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název statě ve sborníku

    7th International Conference on Biomedical and Bioinformatics Engineering

  • ISBN

    978-1-4503-8822-1

  • ISSN

    2153-1633

  • e-ISSN

  • Počet stran výsledku

    7

  • Strana od-do

    36-42

  • Název nakladatele

    ACM Press

  • Místo vydání

    New York

  • Místo konání akce

    Kyoto

  • Datum konání akce

    6. 11. 2020

  • Typ akce podle státní příslušnosti

    WRD - Celosvětová akce

  • Kód UT WoS článku