Spherical U-Net For Infant Cortical Surface Parcellation

Abstract

In human brain MRI studies, it is of great importance to accurately parcellate cortical surfaces into anatomically and functionally meaningful regions. In this paper, we propose a novel end-to-end deep learning method by formulating surface parcellation as a semantic segmentation task on the sphere. To extend the convolutional neural networks (CNNs) to the spherical space, corresponding operations of surface convolution, pooling and upsampling are first developed to deal with data representation on spherical surface meshes, and then spherical CNNs are constructed accordingly. Specifically, the U-Net and SegNet architectures are transformed to the spherical representation for neonatal cortical surface parcellation. Experimental results on 90 neonates indicate the effectiveness and efficiency of our proposed spherical U-Net, in comparison with the spherical SegNet and the previous patch-wise classification method.

Automatic cortical surface parcellation in the fetal brain using attention-gated spherical U-net

“…We compared the automatic parcellation performance of the proposed attention-gated spherical U-net with surface registration-based parcellation ( Fischl et al, 2004 ), SPHARM-net ( Ha and Lyu, 2022 ), and original spherical U-net ( Zhao et al, 2019 ). For surface registration-based parcellation, we aligned a 29 GA template surface with predefined regional labels, constructed with a different TD fetal cohort ( Serag et al, 2012 ; Yun et al, 2019 ), to the individual cortical surface using a 2D sphere-to-sphere non-rigid warping ( Robbins et al, 2004 ).…”

“…As manual cortical parcellation is labor-intensive, reliant on expert knowledge, and time-consuming, several automated methods have been proposed for sulcal/gyral parcellations on adult and infant cortical surfaces from MRI ( Fischl et al, 2004 ; Lyttelton et al, 2007 ; Destrieux et al, 2010 ; Yeo et al, 2010 ; Li and Shen, 2011 ; Auzias et al, 2016 ; Gopinath et al, 2019 ; Parvathaneni et al, 2019 ; Zhao et al, 2019 , 2021 ; Cheng et al, 2020 ; Hao et al, 2020 ). One prevalent approach to automatic parcellation involves surface registration, where single or probabilistic label maps defined on a reference surface are transferred to the target individual surface following registration ( Fischl et al, 2004 ; Lyttelton et al, 2007 ; Destrieux et al, 2010 ; Yeo et al, 2010 ; Hazlett et al, 2017 ; Wu et al, 2019 ; Yun et al, 2019 ).…”

“…By the re-tessellation onto the icosahedron, the spherical surface could be transformed into a consistent structure with uniform-sampled vertices, which enabled the learning of feature maps on the spherical space with hierarchical CNN architectures ( Zhao et al, 2021 ). As the U-net and its variations have shown state-of-the-art performances in medical segmentation fields ( Ronneberger et al, 2015 ), Zhao et al (2019) proposed a spherical U-net by replacing 2D convolution and pooling operators in the U-net with spherical ring-convolution and pooling. Another recent study proposed a deep learning model named SPHARM-net for cortical surface parcellation ( Ha and Lyu, 2022 ), which introduced spherical harmonics-based convolution filters that can encode all the spectral components without the full harmonic expansion to capture geometric details.…”

Harmonization of Infant Cortical Thickness Using Surface-to-Surface Cycle-Consistent Adversarial Networks

“…First, the spherical U-Net architecture [11] provides an effective Direct Neighbor (DiNe) filter to extend conventional convolutional neural network (CNN) to the cortical surface with an inherent spherical topology. It was originally designed for cortical surface parcellation [10] and achieves state-of-the-art performance, which could be used as a generator for site-to-site cortical surface property map translation. Second, since most neuroimaging studies do not have paired data across sites, the popular image generation technique CycleGAN [12] could be leveraged for the unpaired surface translation.…”