Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ma:2023:10.1109/TMI.2022.3206221,
author = {Ma, Q and Li, L and Robinson, EC and Kainz, B and Rueckert, D and Alansary, A},
doi = {10.1109/TMI.2022.3206221},
journal = {IEEE Transactions on Medical Imaging},
pages = {430--443},
title = {CortexODE: learning cortical surface reconstruction by neural ODEs},
url = {http://dx.doi.org/10.1109/TMI.2022.3206221},
volume = {42},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We present CortexODE, a deep learning framework for cortical surface reconstruction. CortexODE leverages neural ordinary differential 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 5 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.
AU  - Ma,Q
AU  - Li,L
AU  - Robinson,EC
AU  - Kainz,B
AU  - Rueckert,D
AU  - Alansary,A
DO  - 10.1109/TMI.2022.3206221
EP  - 443
PY  - 2023///
SN  - 0278-0062
SP  - 430
TI  - CortexODE: learning cortical surface reconstruction by neural ODEs
T2  - IEEE Transactions on Medical Imaging
UR  - http://dx.doi.org/10.1109/TMI.2022.3206221
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000934156000010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://ieeexplore.ieee.org/document/9888110
UR  - http://hdl.handle.net/10044/1/109011
VL  - 42
ER  -
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