Sports, Special Report, Americas

[rezkaesold]

The common setup to study 3333D cell motility consists of acquiring images of a fixed focal plane placed in the middle of the collagen matrix where the cells are embedded. Note that while the images are 2222D, cells are migrating in 3333D, so they can exit and enter the plane of focus, fixed along with the videos. The manual annotation of cells in such long phase-contrast microscopy time-lapse videos is tedious and non-viable. Hence, implementing a robust computational tool for the automatic quantification of cell morpho-dynamics is critical to deciphering the mechanisms that drive cancer cell migration in metastasis. Deep learning (DL) approaches are considered the state-of-the-art for processing images with large inter-variability and intra-variability. DL segmentation and cell detection for a posterior watershed instance segmentation. DL models for cell segmentation, detection and tracking, and to ease the use of large datasets. Some of the previously mentioned methods (e.g., Mask R-CNN, convolutional LSTM U-Net, cosine-embedding) have high-computational requirements when adapting their implementations to new experiments or data types.




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[wangiskahn2]

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