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Capsule network is a recent new deep network architecture that has beenapplied successfully for medical image segmentation tasks. This work extendscapsule networks for volumetric medical image segmentation with self-supervisedlearning. To improve on the problem of weight initialization compared toprevious capsule networks, we leverage self-supervised learning for capsulenetworks pre-training, where our pretext-task is optimized byself-reconstruction. Our capsule network, SS-3DCapsNet, has a UNet-basedarchitecture with a 3D Capsule encoder and 3D CNNs decoder. Our experiments onmultiple datasets including iSeg-2017, Hippocampus, and Cardiac demonstratethat our 3D capsule network with self-supervised pre-training considerablyoutperforms previous capsule networks and 3D-UNets.

SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data