  This paper presents advancements in automated early-stage prediction of the
success of reprogramming human induced pluripotent stem cells (iPSCs) as a
potential source for regenerative cell therapies.The minuscule success rate of
iPSC-reprogramming of around $ 0.01% $ to $ 0.1% $ makes it labor-intensive,
time-consuming, and exorbitantly expensive to generate a stable iPSC line.
Since that requires culturing of millions of cells and intense biological
scrutiny of multiple clones to identify a single optimal clone. The ability to
reliably predict which cells are likely to establish as an optimal iPSC line at
an early stage of pluripotency would therefore be ground-breaking in rendering
this a practical and cost-effective approach to personalized medicine. Temporal
information about changes in cellular appearance over time is crucial for
predicting its future growth outcomes. In order to generate this data, we first
performed continuous time-lapse imaging of iPSCs in culture using an ultra-high
resolution microscope. We then annotated the locations and identities of cells
in late-stage images where reliable manual identification is possible. Next, we
propagated these labels backwards in time using a semi-automated tracking
system to obtain labels for early stages of growth. Finally, we used this data
to train deep neural networks to perform automatic cell segmentation and
classification. Our code and data are available at
https://github.com/abhineet123/ipsc_prediction.
