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

Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Cicek, Thomas Brox, Denis Wirtz, Arrate Munoz-Barrutia

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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