Tracking leukocytes in intravital time lapse images using 3D cell association learning network

High precision tracking analysis of cell position and motion fields using 3D U-net network models

Cells are the basic units of biological organization, and the quantitative analysis of cellular states is an important topic in medicine and is valuable in revealing the complex mechanisms of microscopic world organisms. In order to better understand cell cycle changes as well as drug actions, we need to track cell migration and division. In this paper, we propose a novel engineering model for tracking cells using cell position and motion fields (CPMF). The training sample does not need to be manually annotated, and we modify and edit it against the ground truth using auxiliary tools. The core idea of the project is to combine detection and correlation, and the cell sequence samples are trained by a U-Net network model composed of 3D CNNs, which can track the migration, division, and entry and exit of cells in the field of view with high accuracy in all directions. The average detection accuracy of the cell coordinates is 98.38% and the average tracking accuracy is 98.70%.

Intravital imaging to study cancer progression and metastasis

Navigation through the bulk tumour, entry into the blood vasculature, survival in the circulation, exit at distant sites and resumption of proliferation are all steps necessary for tumour cells to successfully metastasize. The ability of tumour cells to complete these steps is highly dependent on the timing and sequence of the interactions that these cells have with the tumour microenvironment (TME), including stromal cells, the extracellular matrix and soluble factors. The TME thus plays a major role in determining the overall metastatic phenotype of tumours. The complexity and cause-and-effect dynamics of the TME cannot currently be recapitulated in vitro or inferred from studies of fixed tissue, and are best studied in vivo, in real time and at single-cell resolution. Intravital imaging (IVI) offers these capabilities, and recent years have been a time of immense growth and innovation in the field. Here we review some of the recent advances in IVI of mammalian models of cancer and describe how IVI is being used to understand cancer progression and metastasis, and to develop novel treatments and therapies. We describe new techniques that allow access to a range of tissue and cancer types, novel fluorescent reporters and biosensors that allow fate mapping and the probing of functional and phenotypic states, and the clinical applications that have arisen from applying these techniques, reporters and biosensors to study cancer. We finish by presenting some of the challenges that remain in the field, how to address them and future perspectives.