  We present a conceptually simple framework for object instance segmentation
called Contour Proposal Network (CPN), which detects possibly overlapping
objects in an image while simultaneously fitting closed object contours using
an interpretable, fixed-sized representation based on Fourier Descriptors. The
CPN can incorporate state of the art object detection architectures as backbone
networks into a single-stage instance segmentation model that can be trained
end-to-end. We construct CPN models with different backbone networks, and apply
them to instance segmentation of cells in datasets from different modalities.
In our experiments, we show CPNs that outperform U-Nets and Mask R-CNNs in
instance segmentation accuracy, and present variants with execution times
suitable for real-time applications. The trained models generalize well across
different domains of cell types. Since the main assumption of the framework are
closed object contours, it is applicable to a wide range of detection problems
also outside the biomedical domain. An implementation of the model architecture
in PyTorch is freely available.
