Search for temporal cell segmentation robustness in phase-contrast microscopy videosDownload PDF

21 Apr 2022, 17:38 (edited 04 Jun 2022)MIDL 2022 Short PapersReaders: Everyone
  • Keywords: Cell segmentation, transfer-learning, convlstm, phase-contrast microscopy
  • TL;DR: Transfer learning and recurrent ConvLSTM units to exploit the temporal information to segment 3D migrating cells in phase contrast microscopy videos
  • Abstract: This work presents a deep learning-based workflow to segment cancer cells embedded in $3$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.
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  • Paper Type: novel methodological ideas without extensive validation
  • Primary Subject Area: Segmentation
  • Secondary Subject Area: Transfer Learning and Domain Adaptation
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