Manual lesion segmentation presents major labor and limitations for quantitative MS lesion analysis, and recent improvements in deep learning promise more consistent, fully automatic lesion segmentation. However, convolutional neural networks still rely on learned thresholding of the arbitrary boundaries of diffuse hyperintensities. Therefore, we aimed to develop a new DL framework pairing a CNN and a custom surface feature that could detect hyperintense isocontour in 3 dimensions very sensitively. Our goal is to achieve detection of MS lesions and quantification of lesion hyperintensity volume with a new DL algorithm that combines traditional imaging and a specially designed surface feature.
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