Publication: Search for temporal cell segmentation robustness in phase-contrast microscopy videos
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2022
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Abstract
This work presents a deep learning-based workflow to segment cancer cells embedded in D collagen matrices and imaged with phase-contrast microscopy under low magnification and strong background noise conditions. Due to the experimental and imaging setup, cell and protrusion appearance change largely from frame to frame. We use transfer learning and recurrent convolutional long-short term memory units to exploit the temporal information and provide temporally stable results. Our results show that the proposed approach is robust to weight initialization and training data sampling.
Description
Proceeding of: Medical Imaging with Deep Learning (MIDL 2022), Zürich, Switzerland, 6-8
July 2022
Keywords
Cell segmentation, Transfer-learning, ConvLSTM, Phase-contrast microscopy
Bibliographic citation
Gómez de Mariscal, Estibaliz, et al. Search for temporal cell segmentation robustness in phase-contrast microscopy videos. In: Medical Imaging with Deep Learning (MIDL 2022), Zürich, Switzerland, 6-8 July 2022