Contour Proposal Networks for Biomedical Instance Segmentation
This addresses instance segmentation for biomedical imaging, particularly for cells, with potential broader applications, though it appears incremental as it builds on existing detection architectures.
The authors tackled biomedical instance segmentation by proposing Contour Proposal Networks (CPN), a single-stage model that detects objects and fits contours using Fourier Descriptors, achieving higher accuracy than U-Nets and Mask R-CNNs with real-time variants.
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.