%0 Journal 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
%V 77
%@ 1361-8415
%C Amsterdam [u.a.]
%I Elsevier Science
%M FZJ-2022-01559
%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 a fixed-size 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, CPNs outperform U-Net, Mask R-CNN and StarDist in instance segmentation accuracy. We 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 is closed object contours, it is applicable to a wide range of detection problems also beyond the biomedical domain. An implementation of the model architecture in PyTorch is freely available.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ 35180674
%U <Go to ISI:>//WOS:000912927000003
%R 10.1016/j.media.2022.102371
%U https://juser.fz-juelich.de/record/906623