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Upschulte, E.* ; Harmeling, S.* ; Amunts, K.* ; Dickscheid, T.*

Contour proposal networks for biomedical instance segmentation.

Med. Image Anal. 77:102371 (2022)
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Open Access Hybrid
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Cell Detection ; Cell Segmentation ; Cpn ; Object Detection
Language english
Publication Year 2022
HGF-reported in Year 2022
ISSN (print) / ISBN 1361-8415
e-ISSN 1361-8415
Journal Medical Image Analysis
Quellenangaben Volume: 77, Issue: , Pages: , Article Number: 102371 Supplement: ,
Publisher Elsevier
Publishing Place Radarweg 29, 1043 Nx Amsterdam, Netherlands
Reviewing status Peer reviewed
Institute(s) Helmholtz AI - FZJ (HAI - FZJ)
Grants JARA-HPC on the supercomputer JURECA at Juelich Supercomputing Centre (JSC)
Helmholtz Association's Initiative and Networking Fund through the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL)
"Computational Connectomics" of the German Research Foundation (DFG)
European Union
DOI 10.1016/j.media.2022.102371
WOS ID 000912927000003
Scopus ID 85124513907
Erfassungsdatum 2022-10-31
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