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000906623 1001_ $$0P:(DE-Juel1)177675$$aUpschulte, Eric$$b0$$eCorresponding author$$ufzj
000906623 245__ $$aContour proposal networks for biomedical instance segmentation
000906623 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2022
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000906623 520__ $$aWe 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.
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000906623 7001_ $$0P:(DE-HGF)0$$aHarmeling, Stefan$$b1
000906623 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b2$$ufzj
000906623 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b3$$ufzj
000906623 773__ $$0PERI:(DE-600)1497450-2$$a10.1016/j.media.2022.102371$$gVol. 77, p. 102371 -$$p102371 -$$tMedical image analysis$$v77$$x1361-8415$$y2022
000906623 8564_ $$uhttps://juser.fz-juelich.de/record/906623/files/Upschulte_Medical%20Image%20Analysis_2022.pdf$$yOpenAccess
000906623 8564_ $$uhttps://juser.fz-juelich.de/record/906623/files/Upschulte_arXiv_2021.pdf$$yOpenAccess
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