<p class="jove_content">An object segmentation protocol for orbital computed tomography (CT) images is introduced. The methods of labeling the ground truth of orbital structures by using super-resolution, extracting the volume of interest from CT images, and modeling multi-label segmentation using 2D sequential U-Net for orbital CT images are explained for supervised learning.</p>

application-deep-learning-based-medical-image-segmentation-via

Institution - St. Vincent’s Hospital

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