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 in | Computers in biology and medicine Vol. 196; no. Pt C; p. 110891 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Ltd
01.09.2025
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Subjects | |
Online Access | Get full text |
ISSN | 0010-4825 1879-0534 1879-0534 |
DOI | 10.1016/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. |
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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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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 |
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