Automatic tractography and segmentation using finsler geometry based on higher-order tensor fields

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic tractography and segmentation using finsler geometry based on higher-order tensor fields

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Automatic tractography and segmentation using finsler geometry based on higher-order tensor fields

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10101 - Pure mathematics

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Computer Methods and Programs in Biomedicine

ISSN

0169-2607

e-ISSN

1872-7565

Svazek periodika

240

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

IE - Irsko

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

001022497700001

EID výsledku v databázi Scopus

2-s2.0-85162033011