With sparse scribble labels expanded by a random walk and a closing preprocessing step, a U-Net segments bone implant microtomograms as accurately as when trained on dense expert annotations: Dice 0.751 versus 0.750.
V-net: Fully convolutional neural networks for volumetric medical image segmentation
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Sparse Annotations with Random Walks for U-Net Segmentation of Biodegradable Bone Implants in Synchrotron Microtomograms
With sparse scribble labels expanded by a random walk and a closing preprocessing step, a U-Net segments bone implant microtomograms as accurately as when trained on dense expert annotations: Dice 0.751 versus 0.750.