%0 Electronic Article
%A Upschulte, Eric
%A Harmeling, Stefan
%A Amunts, Katrin
%A Dickscheid, Timo
%T Contour Proposal Networks for Biomedical Instance Segmentation
%J Medical image analysis
%@ 1361-8415
%C Amsterdam [u.a.]
%I Elsevier Science
%M FZJ-2021-02034
%P 102371 -
%D 2022
%X 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.
%F PUB:(DE-HGF)25
%9 Preprint
%U https://juser.fz-juelich.de/record/892373