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 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.
Highlights
In terms of inference speed, CPNR4-R50-Feature Pyramid Network (FPN) outperforms both Mask R-CNN-R50-FPN and U-Net when applied with normal single-precision
The influence of local refinement on inference speed was evaluated for the R50-FPN based Contour Proposal Network (CPN), for which four refinement iterations reduced the result by 0.33 frames per seconds (FPS), when used with singleprecision
We proposed the Contour Proposal Network (CPN), a framework for segmenting object instances by proposing contours which are encoded as interpretable, fixed-sized representations based on Fourier Descriptors
Summary
Motivation Instance segmentation is the task of labeling each pixel in an image with an index that represents distinct objects of predefined object classes. This is different from semantic segmentation, which assigns the object class itself to each pixel, and does not distinguish objects of the same type if their shapes touch or overlap. Many instance segmentation methods define one unique object index per pixel, referring to the foreground object only. This results in an incomplete capture of partially superimposed objects, and to a misrepresentation of their actual shape (as in e.g. Fig. 4g top) which in turn might impair shape-sensible downstream tasks like morphological
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