Search for temporal cell segmentation robustness in phase-contrast microscopy videos
This work addresses cell segmentation and tracking for cancer cell migration studies, but it is incremental as it builds on existing methods with specific adaptations.
The authors tackled the problem of segmenting cancer cells in phase-contrast microscopy videos to study cell morphology changes over time, achieving stable and robust results with their deep learning-based workflow.
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.