ArticleIEEE transactions on medical imaging2018
TRACE: A Topological Graph Representation for Automatic Sulcal Curve Extraction.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Article
- Leveraging Input-Level Feature Deformation With Guided-Attention for Sulcal Labeling.IEEE transactions on medical imaging · 2025Article
- Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets.PloS one · 2025Article
- Comprehensive shape analysis of the cortex in Huntington's disease.Human brain mapping · 2023Article
- Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection.Computers in biology and medicine · 2023Article
- Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- A gyrification analysis approach based on Laplace Beltrami eigenfunction level sets.NeuroImage · 2021Article
- Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training.NeuroImage · 2021Article
- Joint analysis of structural connectivity and cortical surface features: correlates with mild traumatic brain injury.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Joint cortical surface and structural connectivity analysis of Alzheimer's Disease.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis.IEEE transactions on medical imaging · 2020Article
- AUTOMATIC LABELING OF CORTICAL SULCI USING SPHERICAL CONVOLUTIONAL NEURAL NETWORKS IN A DEVELOPMENTAL COHORT.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
- Hierarchical spherical deformation for cortical surface registration.Medical image analysis · 2019Article
- Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks.Journal of neuroscience methods · 2019Article
- A computational method for longitudinal mapping of orientation-specific expansion of cortical surface in infants.Medical image analysis · 2018Article
- Multimodal neuroimaging in pediatric type 1 diabetes: a pilot multisite feasibility study of acquisition quality, motion, and variability.Proceedings of SPIE--the International Society for Optical EngineeringArticle
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5 authors.
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Abstract
A proper geometric representation of the cortical regions is a fundamental task for cortical shape analysis and landmark extraction. However, a significant challenge has arisen due to the highly variable, convoluted cortical folding patterns. In this paper, we propose a novel topological graph representation for automatic sulcal curve extraction (TRACE). In practice, the reconstructed surface suffers from noise influences introduced during image acquisition/surface reconstruction. In the presence of noise on the surface, TRACE determines stable sulcal fundic regions by employing the line simplification method that prevents the sulcal folding pattern from being significantly smoothed out. The sulcal curves are then traced over the connected graph in the determined regions by the Dijkstra's shortest path algorithm. For validation, we used the state-of-the-art surface reconstruction pipelines on a reproducibility data set. The experimental results showed higher reproducibility and robustness to noise in TRACE than the existing method (Li et al. 2010) with over 20% relative improvement in error for both surface reconstruction pipelines. In addition, the extracted sulcal curves by TRACE were well-aligned with manually delineated primary sulcal curves. We also provided a choice of parameters to control quality of the extracted sulcal curves and showed the influences of the parameter selection on the resulting curves.
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