4th order tensors for multi-fiber resolution and segmentation in white matter
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F20%3A00114778" target="_blank" >RIV/00216224:14310/20:00114778 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/20:00354559
Result on the web
<a href="https://dl.acm.org/doi/10.1145/3444884.3444892" target="_blank" >https://dl.acm.org/doi/10.1145/3444884.3444892</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3444884.3444892" target="_blank" >10.1145/3444884.3444892</a>
Alternative languages
Result language
angličtina
Original language name
4th order tensors for multi-fiber resolution and segmentation in white matter
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 7th International Conference on Biomedical and Bioinformatics Engineering (ICBBE ’20)
ISBN
9781450388221
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
36-42
Publisher name
Association for Computing Machinery
Place of publication
New York
Event location
Kyoto
Event date
Nov 6, 2020
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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