Contextual Additive Networks to Efficiently Boost 3D Image Segmentations.

Semantic segmentation for 3D medical images is an important task for medical image analysis which would benefit from more efficient approaches. We propose a 3D segmentation framework of cascaded fully convolutional networks (FCNs) with contextual inputs and additive outputs. Compared to previous contextual cascaded networks the additive output forces each subsequent model to refine the output of previous models in the cascade. We use U-Nets of various complexity as elementary FCNs and demonstrate our method for cartilage segmentation on a large set of 3D magnetic resonance images (MRI) of the knee. We show that a cascade of simple U-Nets may for certain tasks be superior to a single deep and complex U-Net with almost two orders of magnitude more parameters. Our framework also allows greater flexibility in trading-off performance and efficiency during testing and training.