Hauptseite > Publikationsdatenbank > Contour proposal networks for biomedical instance segmentation > print

001     906623
005     20231123201913.0
024 7 _ |a 10.1016/j.media.2022.102371
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037 _ _ |a FZJ-2022-01559
082 _ _ |a 610
100 1 _ |a Upschulte, Eric
|0 P:(DE-Juel1)177675
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245 _ _ |a Contour proposal networks for biomedical instance segmentation
260 _ _ |a Amsterdam [u.a.]
|c 2022
|b Elsevier Science
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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 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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536 _ _ |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
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536 _ _ |a DFG project 347572269 - Heterogenität von Zytoarchitektur, Chemoarchitektur und Konnektivität in einem großskaligen Computermodell der menschlichen Großhirnrinde (347572269)
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700 1 _ |a Harmeling, Stefan
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700 1 _ |a Amunts, Katrin
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700 1 _ |a Dickscheid, Timo
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773 _ _ |a 10.1016/j.media.2022.102371
|g Vol. 77, p. 102371 -
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|t Medical image analysis
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|y 2022
|x 1361-8415
856 4 _ |u https://juser.fz-juelich.de/record/906623/files/Upschulte_Medical%20Image%20Analysis_2022.pdf
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856 4 _ |u https://juser.fz-juelich.de/record/906623/files/Upschulte_arXiv_2021.pdf
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