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      Spherical Deformable U-Net: Application to Cortical Surface Parcellation and Development Prediction

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          Abstract

          Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-related problems for 2D/3D images in the Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have an inherent spherical topology in a manifold space, e.g., the convoluted brain cortical surfaces represented by triangular meshes. There is no consistent neighborhood definition and thus no straightforward convolution/pooling operations for such cortical surface data. In this paper, leveraging the regular and hierarchical geometric structure of the resampled spherical cortical surfaces, we create the 1-ring filter on spherical cortical triangular meshes and accordingly develop convolution/pooling operations for constructing Spherical U-Net for cortical surface data. However, the regular nature of the 1-ring filter makes it inherently limited to model fixed geometric transformations. To further enhance the transformation modeling capability of Spherical U-Net, we introduce the deformable convolution and deformable pooling to cortical surface data and accordingly propose the Spherical Deformable U-Net (SDU-Net). Specifically, spherical offsets are learned to freely deform the 1-ring filter on the sphere to adaptively localize cortical structures with different sizes and shapes. We then apply the SDU-Net to two challenging and scientifically important tasks in neuroimaging: cortical surface parcellation and cortical attribute map prediction. Both applications validate the competitive performance of our approach in accuracy and computational efficiency in comparison with state-of-the-art methods.

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          Author and article information

          Contributors
          Role: Senior Member, IEEE
          Role: Senior Member, IEEE
          Role: Fellow, IEEE
          Role: Senior Member, IEEE
          Journal
          8310780
          20511
          IEEE Trans Med Imaging
          IEEE Trans Med Imaging
          IEEE transactions on medical imaging
          0278-0062
          1558-254X
          10 February 2021
          01 April 2021
          April 2021
          02 April 2021
          : 40
          : 4
          : 1217-1228
          Affiliations
          Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310027, China
          Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA.
          Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
          Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
          Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
          Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
          Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310027, China
          Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
          Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
          Author notes
          Corresponding author: Gang Li, gang_li@ 123456med.unc.edu
          Article
          PMC8016713 PMC8016713 8016713 nihpa1671030
          10.1109/TMI.2021.3050072
          8016713
          33417540
          ca9ed802-c8bd-40b7-9b8d-d09979a4fb7b
          History
          Categories
          Article

          cortical surface,Convolutional Neural Network,parcellation,deformable networks,U-Net,triangular mesh

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