Authors:

Jason Chen

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About Paper:

Transcranial direct current stimulation (tDCS) shows promise as a treatment for neurological diseases; however, improvements must be made to improve consistency. One reason for variability is anatomical differences amongst patients, such as variations in skull thickness and brain shape. Whole-head brain segmentation of individuals can be used to plan targeted treatments that optimize the tDCS parameters. Manual or semi-automatic whole-head brain segmentation is work-intensive and time-inefficient, taking 22-30 hours per individual head. Deep learning models can provide fully automated segmentation tasks to counteract this. This work trained two deep learning segmentation pipelines from the GRACE and SynthSeg models using Magnetic Resonance Imaging (MRI) scans of older adults to evaluate their performances. Our analysis reveals that the GRACE model outperforms SynthSeg in terms of alignment with ground truth annotations, as evidenced by a lower average Hausdorff distance and higher average DICE score. However, it is imperative to contextualize these findings within the framework of differing model architectures to attain a more nuanced understanding of the observed disparities. The use of deep learning models for segmentation can allow for more accurate and rapid parameter estimation in tDCS intervention.

Source:

University of Florida / Jason Chen, Veronica Ramos, Skylar Stolte, Aprinda / 2024

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Co-authors:

Jason Chen