ReSCU-Nets: recurrent U-Nets for segmentation of multidimensional microscopy data

Hawkins, R.; Balaghi, N.; Rothenberg, K. E.; Ly, M.; Fernandez-Gonzalez, R.

doi:10.1101/2024.11.28.625889

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bioRxiv · 10.1101/2024.11.28.625889

ReSCU-Nets: recurrent U-Nets for segmentation of multidimensional microscopy data

Hawkins, R.·Balaghi, N.·Rothenberg, K. E.·Ly, M.·Fernandez-Gonzalez, R.

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Abstract

Segmenting multi-dimensional microscopy data requires high accuracy across many images (e.g. timepoints or Z slices) and is thus a labour-intensive part of biological image processing pipelines. We present ReSCU-Nets, recurrent convolutional neural networks that use the segmentation results from the previous frame as a prompt to segment the current frame. We demonstrate that ReSCU-Nets outperform state-of-the-art image segmentation models in different segmentation tasks on time-lapse microscopy sequences.

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Hawkins, R., Balaghi, N., Rothenberg, K. E., Ly, M., Fernandez-Gonzalez, R.. 2024-12-03. ReSCU-Nets: recurrent U-Nets for segmentation of multidimensional microscopy data. https://doi.org/10.1101/2024.11.28.625889

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