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In comparison to other methods, our method had superior Dice across all labels and competitive metrics on the MR image. We evaluated our method with 50 \(T_1\)-weighted brain MR images \(4\times \) down-sampled with 10 automatically generated labels. To address this, we develop an SSR deep network that takes both an anisotropic LR MR image and its corresponding LR labels as input and produces both a super-resolved MR image and its super-resolved labels as output.
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While resulting images from such methods are quite useful, their corresponding LR labels-derived from either automatic algorithms or human raters-are no longer in correspondence with the super-resolved volume. Our method is inspired by internally trained super-resolution, or self-super-resolution (SSR) techniques that target anisotropic, low-resolution (LR) magnetic resonance (MR) images. We propose a method to jointly super-resolve an anisotropic image volume along with its corresponding voxel labels without external training data. Book series (LNCS, volume 12965) Abstract