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Contour proposal networks for biomedical instance segmentation
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DOI：10.1016/j.media.2022.102371.png)
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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. (c) 2022 The Authors. Published by Elsevier B.V.
Keyword:
Cell detection
Cell segmentation
Object detection
CPN
期刊
IF:
11.8
论文数:
3.9K
被引数:
2.4W
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