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title booktitle year volume series month publisher pdf url abstract layout issn id tex_title firstpage lastpage page order cycles bibtex_editor editor bibtex_author author date address container-title genre issued extras
Uncertainty-Aware Contour Proposal Networks for Cell Segmentation in Multi-Modality High-Resolution Microscopy Images
Proceedings of the NeurIPS Challenge on Cell Segmentation in Muliti-modality Microscopy Images
2022
212
Proceedings of Machine Learning Research
0
PMLR
We present a simple framework for cell segmentation, based on uncertainty-aware Contour Proposal Networks (CPNs). It is designed to provide high segmentation accuracy while remaining computationally efficient, which makes it an ideal solution for high throughput microscopy applications. Each predicted cell is provided with four uncertainty estimations that give information about the localization accuracy of the detected cell boundaries. Such additional insights are valuable for downstream single-cell analysis in microscopy image-based biology and biomedical research. In the context of the NeurIPS 22 Cell Segmentation Challenge, the proposed solution is shown to generalize well in a multi-modality setting, while respecting domain-specific requirements such as focusing on specific cell types. Without an ensemble or test-time augmentation the method achieves an F1 score of 0.8986 on the challenge’s validation set.
inproceedings
2640-3498
upschulte23a
Uncertainty-Aware Contour Proposal Networks for Cell Segmentation in Multi-Modality High-Resolution Microscopy Images
1
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1-12
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Ma, Jun and Xie, Ronald and Gupta, Anubha and Guilherme de Almeida, Jos\'e and Bader, Gary D. and Wang, Bo
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Jun
Ma
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Ronald
Xie
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Anubha
Gupta
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José
Almeida
Guilherme de
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Gary D.
Bader
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Bo
Wang
Upschulte, Eric and Harmeling, Stefan and Amunts, Katrin and Dickscheid, Timo
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Eric
Upschulte
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Stefan
Harmeling
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Katrin
Amunts
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Timo
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2023-06-04
Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images
inproceedings
date-parts
2023
6
4