Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training
| Autor: | Shuxing Bao, Ilwoo Lyu, Lingyan Hao, Silvia A. Bunge, Kevin S. Weiner, Willa Voorhies, Jewelia Yao, Warren D. Taylor, Bennett A. Landman, Jacob A. Miller |
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| Rok vydání: | 2021 |
| Předmět: |
Male
Data Analysis Frontal cortex Computer science Image Processing Convolutional neural network Medical and Health Sciences Field (computer science) 0302 clinical medicine Computer-Assisted Sulcal labeling Image Processing Computer-Assisted Child Training set 05 social sciences Magnetic Resonance Imaging medicine.anatomical_structure Neurology Context encoder Female Lateral prefrontal cortex Shallow sulci Adult Adolescent Cognitive Neuroscience Feature vector Prefrontal Cortex Context (language use) Article 050105 experimental psychology lcsh:RC321-571 Cortical surface 03 medical and health sciences Young Adult Spherical data augmentation medicine Connectome Humans 0501 psychology and cognitive sciences lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Neurology & Neurosurgery business.industry Psychology and Cognitive Sciences Pattern recognition Cortex (botany) Artificial intelligence business 030217 neurology & neurosurgery Neuroanatomy |
| Zdroj: | NeuroImage, Vol 229, Iss, Pp 117758-(2021) NeuroImage |
| Popis: | The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N = 60) and adult (N = 36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex (https://github.com/ilwoolyu/SphericalLabeling). |
| Databáze: | OpenAIRE |
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