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Convolutional neural network MRI segmentation for fast and robust optimization of transcranial electrical current stimulation of the human brain

Carla Sendra-Balcells, View ORCID ProfileRicardo Salvador, Juan B. Pedro, M C Biagi, View ORCID ProfileCharlène Aubinet, Brad Manor, Aurore Thibaut, Steven Laureys, View ORCID ProfileKarim Lekadir, View ORCID ProfileGiulio Ruffini
doi: https://doi.org/10.1101/2020.01.29.924985
Carla Sendra-Balcells
1Neuroelectrics, Av. Tibidabo 47bis, Barcelona, Spain
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Ricardo Salvador
1Neuroelectrics, Av. Tibidabo 47bis, Barcelona, Spain
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Juan B. Pedro
2Starlab Barcelona SL, Av. Tibidabo 47bis, Barcelona, Spain
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M C Biagi
1Neuroelectrics, Av. Tibidabo 47bis, Barcelona, Spain
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Charlène Aubinet
4Coma Science Group, GIGA-Consciousness, University of Liege & Brain Clinic, University Hospital of Liege, Liege, Belgium
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Brad Manor
5Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, USA, Beth Israel Deaconess Medical Center, Boston, MA, USA, Harvard Medical School, Boston, MA, USA
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Aurore Thibaut
4Coma Science Group, GIGA-Consciousness, University of Liege & Brain Clinic, University Hospital of Liege, Liege, Belgium
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Steven Laureys
4Coma Science Group, GIGA-Consciousness, University of Liege & Brain Clinic, University Hospital of Liege, Liege, Belgium
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Karim Lekadir
3Artificial Intelligence in Medicine Lab, Universitat de Barcelona, Dpt. Matemàtiques & Informàtica
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Giulio Ruffini
1Neuroelectrics, Av. Tibidabo 47bis, Barcelona, Spain
2Starlab Barcelona SL, Av. Tibidabo 47bis, Barcelona, Spain
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  • ORCID record for Giulio Ruffini
  • For correspondence: giulio.ruffini@neuroelectrics.com
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Abstract

The segmentation of structural MRI data is an essential step for deriving geometrical information about brain tissues. One important application is in transcranial direct current stimulation (e.g., tDCS), a non-invasive neuromodulatory technique where head modeling is required to determine the electric field (E-field) generated in the cortex to predict and optimize its effects. Here we propose a deep learning-based model (StarNEt) to automatize white matter (WM) and gray matter (GM) segmentation and compare its performance with FreeSurfer, an established tool. Since good definition of sulci and gyri in the cortical surface is an important requirement for E-field calculation, StarNEt is specifically designed to output masks at a higher resolution than that of the original input T1w-MRI. StarNEt uses a residual network as the encoder (ResNet) and a fully convolutional neural network with U-net skip connections as the decoder to segment an MRI slice by slice. Slice vertical location is provided as an extra input. The model was trained on scans from 425 patients in the open-access ADNI+IXI datasets, and using FreeSurfer segmentation as ground truth. Model performance was evaluated using the Dice Coefficient (DC) in a separate subset (N=105) of ADNI+IXI and in two extra testing sets not involved in training. In addition, FreeSurfer and StarNEt were compared to manual segmentations of the MRBrainS18 dataset, also unseen by the model. To study performance in real use cases, first, we created electrical head models derived from the FreeSurfer and StarNEt segmentations and used them for montage optimization with a common target region using a standard algorithm (Stimweaver) and second, we used StarNEt to successfully segment the brains of minimally conscious state (MCS) patients having suffered from brain trauma, a scenario where FreeSurfer typically fails. Our results indicate that StarNEt matches FreeSurfer performance on the trained tasks while reducing computation time from several hours to a few seconds, and with the potential to evolve into an effective technique even when patients present large brain abnormalities.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 29, 2020.
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Convolutional neural network MRI segmentation for fast and robust optimization of transcranial electrical current stimulation of the human brain
Carla Sendra-Balcells, Ricardo Salvador, Juan B. Pedro, M C Biagi, Charlène Aubinet, Brad Manor, Aurore Thibaut, Steven Laureys, Karim Lekadir, Giulio Ruffini
bioRxiv 2020.01.29.924985; doi: https://doi.org/10.1101/2020.01.29.924985
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Convolutional neural network MRI segmentation for fast and robust optimization of transcranial electrical current stimulation of the human brain
Carla Sendra-Balcells, Ricardo Salvador, Juan B. Pedro, M C Biagi, Charlène Aubinet, Brad Manor, Aurore Thibaut, Steven Laureys, Karim Lekadir, Giulio Ruffini
bioRxiv 2020.01.29.924985; doi: https://doi.org/10.1101/2020.01.29.924985

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