SPHARM-Net: Spherical Harmonics-based Convolutional Neural Network

[Software]

SPHARM-Net is a spherical harmonics-based convolutional neural network for vertex-wise inference.

A rotation-equivariant convolutional filter can avoid the need for rigid data augmentation, and rotational equivariance can be achieved in spectral convolution without requiring a specific neighborhood definition. However, the limited resources of modern machines enable only band-limited spectral components that may result in the loss of geometric details.

SPHARM-Net seeks to use a constrained spherical convolutional filter that supports an infinite set of spectral components. The proposed filter encodes all spectral components without relying on the full harmonic expansion to capture geometric details. The use of rotational equivariance significantly reduces training time for vertex-wise inference. The proposed convolution is fully composed of matrix transformations, offering efficient and fast spectral processing. While initially tested on brain data, SPHARM-Net has the potential to be extended for processing any spherical data.

• Ha, S., Lyu, I., SPHARM-Net: Spherical Harmonics-based Convolution for Cortical Parcellation. IEEE Transactions on Medical Imaging, 41(10), 2739-2751, 2022  [link]

SphericalLabeling: Cortical Surface Labeling using Spherical Data Augmentation and Context-aware Training

[Software]

We adapted spherical convolutional neural networks designed for generic semantic segmentation tasks to our sulcal labeling task. However, directly using the generic networks for our task is challenging due to two main reasons: limited training samples that capture anatomical variability and a lack of hierarchical neuroanatomical association. To adapt the generic networks to our specific problem, we propose spherical data augmentation and context-aware training. These techniques allow us to efficiently utilize existing training samples and accept a wide range of individual variability by considering training data synthesis and contextual information.

• Lyu, I., Bao, S., Hao, L., Yao, J., Miller, J., Voorhies, W., Taylor, W., Bunge, S., Weiner, K., Landman, B., Labeling Lateral Prefrontal Sulci using Spherical Data Augmentation and Context-aware Training. NeuroImage, 229, 117758, 2021 [link]

• Hao, L., Bao, S., Tang, Y., Gao, R., Parvathaneni, P., Miller, J., Voorhies, W., Yao, J., Bunge, S., Weiner, K., Landman, B., Lyu, I., Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort. IEEE International Symposium on Biomedical Imaging (ISBI) 2020, IEEE, 412-415, Iowa City, Iowa, USA, 2020 [link]

• Parvathaneni, P., Bao, S., Nath, V., Woodward, N., Claassen, D., Cascio, C., Zald, D., Huo, Y., Landman, B., Lyu, I., Cortical Surface Parcellation using Spherical Convolutional Neural Networks, Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019, LNCS11766, 501-509, Shenzhen, China, 2019 [link]

Applications: Automatic Parcellation & Sulcal Delineation

• Ha, S., Lyu, I., SPHARM-Net: Spherical Harmonics-based Convolution for Cortical Parcellation. IEEE Transactions on Medical Imaging, 41(10), 2739-2751, 2022  [link]

• Lyu, I., Bao, S., Hao, L., Yao, J., Miller, J., Voorhies, W., Taylor, W., Bunge, S., Weiner, K., Landman, B., Labeling Lateral Prefrontal Sulci using Spherical Data Augmentation and Context-aware Training. NeuroImage, 229, 117758, 2021 [link]