A simulator for the validation of tractography-based cortical surface parcellations

Diffusion Magnetic Resonance Imaging maps the movement of water molecules, revealing the structure of White Matter (WM). Tractography reconstructs the main WM pathways as 3D curves, referred to as brain fibers. Using cortical parcellation into connected regions is crucial for studying the structural...

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Published inComputers in biology and medicine Vol. 196; no. Pt C; p. 110891
Main Authors Poo, Elida, Molina, Joaquín, Mangin, Jean-François, Hernández, Cecilia, Guevara, Pamela
Format Journal Article
LanguageEnglish
Published United States Elsevier Ltd 01.09.2025
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Online AccessGet full text
ISSN0010-4825
1879-0534
1879-0534
DOI10.1016/j.compbiomed.2025.110891

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Abstract Diffusion Magnetic Resonance Imaging maps the movement of water molecules, revealing the structure of White Matter (WM). Tractography reconstructs the main WM pathways as 3D curves, referred to as brain fibers. Using cortical parcellation into connected regions is crucial for studying the structural connectome defined by WM connections. Tractography-based parcellation methods aim to define brain parcels by analyzing WM fiber connectivity profiles. However, these methods lack ground truth, challenging their comparison, validation, and improvements. This work proposes a method to generate simulated data as ground truth for evaluating tractography-based parcellation methods. The simulated data consists of random parcels with their connections. First, the method subdivides the cortical surface into random parcels using geodesic distance. Then, it simulates the connections between parcels utilizing a fiber bundle simulator extension based on spline curves, with the ends of the bundles adapted to the shapes of the connected parcels. We used the simulator to generate a simulated dataset of 20 subjects based on a random parcellation, with 150 parcels per hemisphere, which we used to evaluate a tractography-based parcellation algorithm under different input parameters. Results showed up to 118 similar parcels per hemisphere, using a Dice coefficient of 0.5 or higher. This simulator represents the first tool for validating tractography-based parcellation algorithms, offering a realistic and flexible ground truth for their evaluation. •We simulated data to evaluate tractography-based cortical parcellation algorithms.•Cortical parcels are simulated using a random geodesic distance-based parcellation.•Connections between parcels are simulated using spline curves and bundle centroids.•This simulator is the first to provide ground truth data for this application.
AbstractList Diffusion Magnetic Resonance Imaging maps the movement of water molecules, revealing the structure of White Matter (WM). Tractography reconstructs the main WM pathways as 3D curves, referred to as brain fibers. Using cortical parcellation into connected regions is crucial for studying the structural connectome defined by WM connections. Tractography-based parcellation methods aim to define brain parcels by analyzing WM fiber connectivity profiles. However, these methods lack ground truth, challenging their comparison, validation, and improvements. This work proposes a method to generate simulated data as ground truth for evaluating tractography-based parcellation methods. The simulated data consists of random parcels with their connections. First, the method subdivides the cortical surface into random parcels using geodesic distance. Then, it simulates the connections between parcels utilizing a fiber bundle simulator extension based on spline curves, with the ends of the bundles adapted to the shapes of the connected parcels. We used the simulator to generate a simulated dataset of 20 subjects based on a random parcellation, with 150 parcels per hemisphere, which we used to evaluate a tractography-based parcellation algorithm under different input parameters. Results showed up to 118 similar parcels per hemisphere, using a Dice coefficient of 0.5 or higher. This simulator represents the first tool for validating tractography-based parcellation algorithms, offering a realistic and flexible ground truth for their evaluation.Diffusion Magnetic Resonance Imaging maps the movement of water molecules, revealing the structure of White Matter (WM). Tractography reconstructs the main WM pathways as 3D curves, referred to as brain fibers. Using cortical parcellation into connected regions is crucial for studying the structural connectome defined by WM connections. Tractography-based parcellation methods aim to define brain parcels by analyzing WM fiber connectivity profiles. However, these methods lack ground truth, challenging their comparison, validation, and improvements. This work proposes a method to generate simulated data as ground truth for evaluating tractography-based parcellation methods. The simulated data consists of random parcels with their connections. First, the method subdivides the cortical surface into random parcels using geodesic distance. Then, it simulates the connections between parcels utilizing a fiber bundle simulator extension based on spline curves, with the ends of the bundles adapted to the shapes of the connected parcels. We used the simulator to generate a simulated dataset of 20 subjects based on a random parcellation, with 150 parcels per hemisphere, which we used to evaluate a tractography-based parcellation algorithm under different input parameters. Results showed up to 118 similar parcels per hemisphere, using a Dice coefficient of 0.5 or higher. This simulator represents the first tool for validating tractography-based parcellation algorithms, offering a realistic and flexible ground truth for their evaluation.
Diffusion Magnetic Resonance Imaging maps the movement of water molecules, revealing the structure of White Matter (WM). Tractography reconstructs the main WM pathways as 3D curves, referred to as brain fibers. Using cortical parcellation into connected regions is crucial for studying the structural connectome defined by WM connections. Tractography-based parcellation methods aim to define brain parcels by analyzing WM fiber connectivity profiles. However, these methods lack ground truth, challenging their comparison, validation, and improvements. This work proposes a method to generate simulated data as ground truth for evaluating tractography-based parcellation methods. The simulated data consists of random parcels with their connections. First, the method subdivides the cortical surface into random parcels using geodesic distance. Then, it simulates the connections between parcels utilizing a fiber bundle simulator extension based on spline curves, with the ends of the bundles adapted to the shapes of the connected parcels. We used the simulator to generate a simulated dataset of 20 subjects based on a random parcellation, with 150 parcels per hemisphere, which we used to evaluate a tractography-based parcellation algorithm under different input parameters. Results showed up to 118 similar parcels per hemisphere, using a Dice coefficient of 0.5 or higher. This simulator represents the first tool for validating tractography-based parcellation algorithms, offering a realistic and flexible ground truth for their evaluation. •We simulated data to evaluate tractography-based cortical parcellation algorithms.•Cortical parcels are simulated using a random geodesic distance-based parcellation.•Connections between parcels are simulated using spline curves and bundle centroids.•This simulator is the first to provide ground truth data for this application.
Diffusion Magnetic Resonance Imaging maps the movement of water molecules, revealing the structure of White Matter (WM). Tractography reconstructs the main WM pathways as 3D curves, referred to as brain fibers. Using cortical parcellation into connected regions is crucial for studying the structural connectome defined by WM connections. Tractography-based parcellation methods aim to define brain parcels by analyzing WM fiber connectivity profiles. However, these methods lack ground truth, challenging their comparison, validation, and improvements. This work proposes a method to generate simulated data as ground truth for evaluating tractography-based parcellation methods. The simulated data consists of random parcels with their connections. First, the method subdivides the cortical surface into random parcels using geodesic distance. Then, it simulates the connections between parcels utilizing a fiber bundle simulator extension based on spline curves, with the ends of the bundles adapted to the shapes of the connected parcels. We used the simulator to generate a simulated dataset of 20 subjects based on a random parcellation, with 150 parcels per hemisphere, which we used to evaluate a tractography-based parcellation algorithm under different input parameters. Results showed up to 118 similar parcels per hemisphere, using a Dice coefficient of 0.5 or higher. This simulator represents the first tool for validating tractography-based parcellation algorithms, offering a realistic and flexible ground truth for their evaluation.
ArticleNumber 110891
Author Molina, Joaquín
Mangin, Jean-François
Poo, Elida
Guevara, Pamela
Hernández, Cecilia
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Keywords Tractography-based parcellation
Spline curves
White Matter
Fiber bundle simulator
Cortical parcellation
Tractography
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Snippet Diffusion Magnetic Resonance Imaging maps the movement of water molecules, revealing the structure of White Matter (WM). Tractography reconstructs the main WM...
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StartPage 110891
SubjectTerms Adult
Algorithms
Cerebral Cortex - diagnostic imaging
Computer Simulation
Connectome - methods
Cortical parcellation
Diffusion Tensor Imaging - methods
Female
Fiber bundle simulator
Humans
Image Processing, Computer-Assisted - methods
Male
Spline curves
Tractography
Tractography-based parcellation
White Matter
White Matter - diagnostic imaging
Title A simulator for the validation of tractography-based cortical surface parcellations
URI https://www.clinicalkey.com/#!/content/1-s2.0-S0010482525012430
https://dx.doi.org/10.1016/j.compbiomed.2025.110891
https://www.ncbi.nlm.nih.gov/pubmed/40812013
https://www.proquest.com/docview/3239784160
Volume 196
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