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Transfer learning framework for cell segmentation with incorporation of geometric features

Yinuo Jin, Alexandre Toberoff, Elham Azizi
doi: https://doi.org/10.1101/2021.02.28.433289
Yinuo Jin
1Department of Computer Science, Columbia University, New York, NY, USA
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Alexandre Toberoff
1Department of Computer Science, Columbia University, New York, NY, USA
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Elham Azizi
2Department of Biomedical Engineering and Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
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  • For correspondence: ea2690@columbia.edu
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Abstract

With recent advances in multiplexed imaging and spatial transcriptomic and proteomic technologies, cell segmentation is becoming a crucial step in biomedical image analysis. In recent years, Fully Convolutional Networks (FCN) have achieved great success in nuclei segmentation in in vitro imaging. Nevertheless, it remains challenging to perform similar tasks on in situ tissue images with more cluttered cells of diverse shapes. To address this issue, we propose a novel transfer learning, cell segmentation framework incorporating shape-aware features in a deep learning model, with multi-level watershed and morphological post-processing steps. Our results show that incorporation of geometric features improves generalizability to segmenting cells in in situ tissue images, using solely in vitro images as training data.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • yj2589{at}columbia.edu, aat2167{at}columbia.edu

  • LMRL Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020)

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted March 10, 2021.
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Transfer learning framework for cell segmentation with incorporation of geometric features
Yinuo Jin, Alexandre Toberoff, Elham Azizi
bioRxiv 2021.02.28.433289; doi: https://doi.org/10.1101/2021.02.28.433289
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Transfer learning framework for cell segmentation with incorporation of geometric features
Yinuo Jin, Alexandre Toberoff, Elham Azizi
bioRxiv 2021.02.28.433289; doi: https://doi.org/10.1101/2021.02.28.433289

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