medical-6

Brain Morphometry

3260 papers • 126 benchmarks • 313 datasets

Measurement of brain structures from neuroimaging (MRI).

(Image credit: Papersgraph)

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Most implemented papers

Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation

M. Reyes, R. Wiest, M. Rebsamen, C. Rummel, Richard McKinley•Tue Aug 11 2020

DL+DiReCT is a promising combination of a deep learning‐based method with a traditional registration technique to detect subtle changes in cortical thickness, and experiments suggest that both DiReCT‐based methods had higher sensitivity to changes in cortex thickness than Freesurfer.

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Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning

Yannick Suter, M. Reyes, R. Wiest, M. Rebsamen, C. Rummel•Tue Apr 07 2020

The general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds is demonstrated.

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Multiple Instance Neuroimage Transformer

Yuyin Zhou, Ayush Singla, Qingyu Zhao, Daniel K. Do, K. Pohl, Ehsan Adeli•Thu Aug 18 2022

The efficacy of the multiple instance learning based convolution-free transformer model, called Multiple Instance Neuroimage Transformer (MINiT), for the classification of T1-weighted (T1w) MRIs is evaluated by training it to identify sex from T1w-MRIs of two public datasets.

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Reliable brain morphometry from contrast‐enhanced T1w‐MRI in patients with multiple sclerosis

M. Reyes, R. Wiest, M. Rebsamen, C. Rummel, Richard McKinley, Piotr Radojewski, M. Pistor, C. Friedli, R. Hoepner, A. Salmen, A. Chan, F. Wagner•Sun Oct 16 2022

It is hypothesized that deep learning (DL)‐based morphometry methods can extract morphometric measures also from contrast‐enhanced MRI, making additional cases available for analysis and potential future diagnostic morphometry tools.

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