TY  - EJOUR
AU  - Upschulte, Eric
AU  - Harmeling, Stefan
AU  - Amunts, Katrin
AU  - Dickscheid, Timo
TI  - Contour Proposal Networks for Biomedical Instance Segmentation
JO  - Medical image analysis
SN  - 1361-8415
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - FZJ-2021-02034
SP  - 102371 -
PY  - 2022
AB  - 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.
LB  - PUB:(DE-HGF)25
UR  - https://juser.fz-juelich.de/record/892373
ER  -