Search for temporal cell segmentation robustness in phase-contrast microscopy videos

• URL: http://arxiv.org/abs/2112.08817v1
• Date: Thu, 16 Dec 2021 12:03:28 GMT
• Title: Search for temporal cell segmentation robustness in phase-contrast microscopy videos
• Authors: Estibaliz G\'omez-de-Mariscal, Hasini Jayatilaka, \"Ozg\"un \c{C}i\c{c}ek, Thomas Brox, Denis Wirtz, Arrate Mu\~noz-Barrutia
• Abstract summary: In this work, we present a deep learning-based workflow to segment cancer cells embedded in 3D collagen matrices. We also propose a geometrical-characterization approach to studying cancer cell morphology. We introduce a new annotated dataset for 2D cell segmentation and tracking, and an open-source implementation to replicate the experiments or adapt them to new image processing problems.
• Score: 31.92922565397439
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Studying cell morphology changes in time is critical to understanding cell migration mechanisms. In this work, we present a deep learning-based workflow to segment cancer cells embedded in 3D collagen matrices and imaged with phase-contrast microscopy. Our approach uses transfer learning and recurrent convolutional long-short term memory units to exploit the temporal information from the past and provide a consistent segmentation result. Lastly, we propose a geometrical-characterization approach to studying cancer cell morphology. Our approach provides stable results in time, and it is robust to the different weight initialization or training data sampling. We introduce a new annotated dataset for 2D cell segmentation and tracking, and an open-source implementation to replicate the experiments or adapt them to new image processing problems.

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