Related Researcher

Author's Photo

Lyu, Ilwoo
3D Shape Analysis Lab
Research Interests
  • 3D Shape Analysis
  • Image Processing
  • Computer Vision
  • Machine Learning
  • Medical Image Analysis


Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training

Cited 0 times inthomson ciCited 0 times inthomson ci
Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training
Lyu, IlwooBao, S.Hao, L.Yao, J.Miller, J.A.Voorhies, W.Taylor, W.D.Bunge, S.A.Weiner, K.S.Landman, B.A.
Issue Date
Academic Press Inc.
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 ( © 2021 The Author(s)
Appears in Collections:
CSE_Journal Papers
Files in This Item:
1-s2.0-S1053811921000355-main.pdf Download

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record


  • mendeley


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.