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Fast deep learning pipeline for neonatal cortical surface extraction

Paper Authors:

Qiang Ma,

Kaili Liang,

Liu Li,

Saga Masui,

Yourong Guo,

Chiara Nosarti,

Emma C. Robinson,

Bernhard Kainz,

Daniel Rueckert

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Key Details

Proposes fast DL pipeline for neonatal cortical surface extraction

Uses multiscale network to predict surfaces end-to-end

Incorporates GPU acceleration for runtime of 24 seconds

Achieves superior/equal surface quality in 82.5% test cases

AI generated summary

Fast deep learning pipeline for neonatal cortical surface extraction

This paper proposes a fast deep learning pipeline to extract cortical surfaces from neonatal brain MRIs in the Developing Human Connectome Project dataset. A multiscale deformation network is introduced to predict cortical surfaces end-to-end without requiring tissue segmentation. This pipeline incorporates GPU-accelerated processing and completes within 24 seconds, nearly 1000 times faster than the original 6.5 hour pipeline. Manual evaluation shows this pipeline produces superior or equal cortical surface quality for 82.5% of test cases.

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