CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs

• URL: http://arxiv.org/abs/2202.08329v1
• Date: Wed, 16 Feb 2022 20:57:59 GMT
• Title: CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs
• Authors: Qiang Ma, Liu Li, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary
• Abstract summary: CortexODE is a deep learning framework for cortical surface reconstruction. It can be integrated to an automatic learning-based pipeline, which reconstructs surfaces efficiently in less than 6 seconds. Our experiments demonstrate that the CortexODE-based pipeline can achieve less than 0.2mm average geometric error.
• Score: 12.239318066719068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present CortexODE, a deep learning framework for cortical surface reconstruction. CortexODE leverages neural ordinary different equations (ODEs) to deform an input surface into a target shape by learning a diffeomorphic flow. The trajectories of the points on the surface are modeled as ODEs, where the derivatives of their coordinates are parameterized via a learnable Lipschitz-continuous deformation network. This provides theoretical guarantees for the prevention of self-intersections. CortexODE can be integrated to an automatic learning-based pipeline, which reconstructs cortical surfaces efficiently in less than 6 seconds. The pipeline utilizes a 3D U-Net to predict a white matter segmentation from brain Magnetic Resonance Imaging (MRI) scans, and further generates a signed distance function that represents an initial surface. Fast topology correction is introduced to guarantee homeomorphism to a sphere. Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively. The proposed pipeline is evaluated on large-scale neuroimage datasets in various age groups including neonates (25-45 weeks), young adults (22-36 years) and elderly subjects (55-90 years). Our experiments demonstrate that the CortexODE-based pipeline can achieve less than 0.2mm average geometric error while being orders of magnitude faster compared to conventional processing pipelines.

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