Contour Proposal Networks for Biomedical Instance Segmentation

• URL: http://arxiv.org/abs/2104.03393v1
• Date: Wed, 7 Apr 2021 21:00:45 GMT
• Title: Contour Proposal Networks for Biomedical Instance Segmentation
• Authors: Eric Upschulte, Stefan Harmeling, Katrin Amunts and Timo Dickscheid
• Abstract summary: We present a conceptually simple framework for object instance segmentation called Contour Proposal Network (CPN) CPN detects possibly overlapping objects in an image while simultaneously fitting closed object contours using an interpretable, fixed-sized representation based on Fourier Descriptors. 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.
• Score: 0.8602553195689513
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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