mlsanityData to 14 Oct 2026

Towards Early Prediction of Human iPSC Reprogramming Success

arXiv 2305.14575 ↗First posted 23 May 2023cs.CVcited by 2

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Abstract (arXiv)

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/ipscprediction.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 64 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

20 of 64 references.

References · 20

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8 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Improving Token-based Object Detection with Videocited by 1
  2. 2026Tokenizing Semantic Segmentation with Run Length Encodingcited by 0