LayeredCNN: Segmenting Layers with Autoregressive Models

Jakob L. Christensen, Patrick Møller Jensen, Morten Rieger Hannemose, Anders Bjorholm Dahl, Vedrana Andersen Dahl

Abstract: We address a subclass of segmentation problems where the labels of the image are structured in layers. We propose applying autoregressive CNNs which, when given an image and a partial segmentation of layers, complete the segmentation. Initializing the model with a user-provided partial segmentation allows for choosing which layers the model should segment. Alternatively, the model can produce an automatic initialization, albeit with some performance loss. The model is trained exclusively on synthetic data from our data generation algorithm. It yields impressive performance on the synthetic data and generalizes to real data it has never seen.