Hauptseite > Publikationsdatenbank > Contour Proposal Networks for Biomedical Instance Segmentation > print

001     892373
005     20250813093043.0
024 7 _ |2 arXiv
|a arXiv:2104.03393
024 7 _ |2 Handle
|a 2128/27736
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|a altmetric:103541910
037 _ _ |a FZJ-2021-02034
082 _ _ |a 610
100 1 _ |0 P:(DE-Juel1)177675
|a Upschulte, Eric
|b 0
|e Corresponding author
|u fzj
245 _ _ |a Contour Proposal Networks for Biomedical Instance Segmentation
260 _ _ |a Amsterdam [u.a.]
|b Elsevier Science
|c 2022
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520 _ _ |a 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.
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536 _ _ |0 G:(EU-Grant)945539
|a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
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700 1 _ |0 P:(DE-HGF)0
|a Harmeling, Stefan
|b 1
700 1 _ |0 P:(DE-Juel1)131631
|a Amunts, Katrin
|b 2
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700 1 _ |0 P:(DE-Juel1)165746
|a Dickscheid, Timo
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773 _ _ |0 PERI:(DE-600)1497450-2
|g p. 102371 -
|p 102371 -
|t Medical image analysis
|x 1361-8415
|y 2022
856 4 _ |u https://juser.fz-juelich.de/record/892373/files/Invoice_OAD0000186910.pdf
856 4 _ |u https://juser.fz-juelich.de/record/892373/files/Upschulte_arXiv_2021.pdf
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