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

21 Apr 2022, 17:38 (modified: 04 Jun 2022, 12:07)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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