Automatic tractography and segmentation using finsler geometry based on higher-order tensor fields
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00131278" target="_blank" >RIV/00216224:14310/23:00131278 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.cmpb.2023.107630" target="_blank" >https://doi.org/10.1016/j.cmpb.2023.107630</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cmpb.2023.107630" target="_blank" >10.1016/j.cmpb.2023.107630</a>
Alternative languages
Result language
angličtina
Original language name
Automatic tractography and segmentation using finsler geometry based on higher-order tensor fields
Original language description
Background and objective: We focus on three-dimensional higher-order tensorial (HOT) images using Finsler geometry. In biomedical image analysis, these images are widely used, and they are based on the diffusion profiles inside the voxels. The diffusion information is stored in the so-called diffusion tensor D . Our objective is to present new methods revealing the architecture of neural fibers in presence of cross-ings and high curvatures. After tracking the fibers, we achieve direct 3D image segmentation to analyse the brain's white matter structures. Methods: To deal with the construction of the underlying fibers, the inverse of the second-order diffusion tensor D , understood as the metric tensor D -1, is commonly used in DTI modality. For crossing and highly curved fibers, higher order tensors are more relevant, but it is challenging to find an analogue of such an inverse in the HOT case. We employ an innovative approach to metrics based on higher order tensors to track the fibers properly. We propose to feed the tracked fibers as the internal initial contours in an efficient version of 3D segmentation. Results: We propose a brand-new approach to the inversion of a diffusion HOT, and an effective way of fiber tracking in the Finsler setting, based on innovative classification of the individual voxels. Thus, we can handle complex structures with high curvatures and crossings, even in the presence of noise. Based on our novel tractog-raphy approach, we also introduce a new segmentation method. We feed the detected fibers as the initial position of the contour surfaces to segment the image using a relevant active contour method (i.e., initi-ating the segmentation from inside the structures). Conclusions: This is a pilot work, enhancing methods for fiber tracking and segmentation. The implemented algorithms were successfully tested on both syn-thetic and real data. The new features make our algorithms robust and fast, and they allow distinguishing individual objects in complex structures, even under noise.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10101 - Pure mathematics
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)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computer Methods and Programs in Biomedicine
ISSN
0169-2607
e-ISSN
1872-7565
Volume of the periodical
240
Issue of the periodical within the volume
October
Country of publishing house
IE - IRELAND
Number of pages
14
Pages from-to
1-14
UT code for WoS article
001022497700001
EID of the result in the Scopus database
2-s2.0-85162033011