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@article{2298407,
author = {Bansal, Avinash and Kaushik, Sumit and Bihonegn, Temesgen Tsegaye and Slovák, Jan},
article_location = {Clare},
article_number = {October},
doi = {http://dx.doi.org/10.1016/j.cmpb.2023.107630},
keywords = {HARDI; Tractography; Segmentation; HOT inversion; Finsler geometry; White matter structure},
language = {eng},
issn = {0169-2607},
journal = {Computer Methods and Programs in Biomedicine},
title = {Automatic tractography and segmentation using finsler geometry based on higher-order tensor fields},
url = {https://doi.org/10.1016/j.cmpb.2023.107630},
volume = {240},
year = {2023}
}
TY - JOUR
ID - 2298407
AU - Bansal, Avinash - Kaushik, Sumit - Bihonegn, Temesgen Tsegaye - Slovák, Jan
PY - 2023
TI - Automatic tractography and segmentation using finsler geometry based on higher-order tensor fields
JF - Computer Methods and Programs in Biomedicine
VL - 240
IS - October
SP - 1-14
EP - 1-14
PB - Elsevier Ireland Ltd.
SN - 01692607
KW - HARDI
KW - Tractography
KW - Segmentation
KW - HOT inversion
KW - Finsler geometry
KW - White matter structure
UR - https://doi.org/10.1016/j.cmpb.2023.107630
N2 - 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.
ER -
BANSAL, Avinash, Sumit KAUSHIK, Temesgen Tsegaye BIHONEGN and Jan SLOVÁK. Automatic tractography and segmentation using finsler geometry based on higher-order tensor fields. \textit{Computer Methods and Programs in Biomedicine}. Clare: Elsevier Ireland Ltd., 2023, vol.~240, October, p.~1-14. ISSN~0169-2607. Available from: https://dx.doi.org/10.1016/j.cmpb.2023.107630.
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