We develop object detection aplication based on deep learning methods. The models automatically identify pixels to its objects and segment the salient part. The segmented images are feed to pertrained model for classification.
A Unet-based method with three-dimensional filters is constructed and applied to brain MRI. The tool also included brain extraction, inhomogeneity correction and N4 bias correction.
We propose a deep learning approach to automatically identify DWMA regions on T2-weighted MRI images. Specifically, we formulated DWMA detection as an image voxel classification task; that is, the voxels on T2-weighted images are treated as samples and exclusively assigned as DWMA or normal white matter voxel classes.
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