June, 2019

An overview of the proposed method. Three geometric features (iH, SD, H) are used for training the spherical U-Net to predict 49 cortical parcellation labels. For each geometric property, intermediate deformation fields draw a total of 11+11 respective samples after boundary and geometric alignment for data augmentation. The cortical parcellation is then performed using the original geometric features of testing subjects.

Cortical Surface Parcellation using Spherical Convolutional Neural Networks

Jun. 6, 2019—*Prasanna Parvathaneni, *Shunxing Bao, Vishwesh Nath, David H. Zald, Neil D. Woodward, Daniel O. Claassen, Carissa Cascio, Yuankai Huo, Bennett A. Landman, Ilwoo Lyu, “Cortical Surface Parcellation using Spherical Convolutional Neural Networks”, MICCAI 2019, LNCS11766, 501-509. Abstract We present cortical surface parcellation using spherical deep convolutional neural networks. Traditional multi-atlas cortical surface parcellation requires inter-subject surface registration using geometric features with high processing...