Deep learning-based automated cell tracking (DeepACT) for identification of human keratinocyte stem cells. (IMAGE)

Tokyo Medical and Dental University

Deep learning-based automated cell tracking (DeepACT) for identification of human keratinocyte stem cells.

Caption

DeepACT comprises two main modules: identifying human keratinocytes at single-cell resolution from phase-contrast images of cultures through deep learning and tracking keratinocyte motion in the colony using a state-space model. As human keratinocyte stem cell colonies exhibits a unique motion pattern, DeepACT can distinguish keratinocyte stem cell colonies from non-stem cell-derived colonies by analyzing the spatial and velocity information of cells. This system can be widely applied to stem cell cultures used in regenerative medicine and provides a platform for developing reliable and noninvasive quality control technology.

Credit

Department of Stem Cell Biology,TMDU

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