Center-Based iPSC Colony Counting with Multi-Task Learning

Mirza Tanzim Sami, Da Yan, Bhadhan Roy Joy, Jalal Khalil, Ricardo Cevallos, Md Emon Hossain, Kejin Hu, Yang Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


iPSCs are pluripotent stem cells generated from adult tissue through a process called cellular reprogramming. However, cellular reprogramming is a lengthy and inefficient process since only a small fraction of cells can reliably become iPSCs. The reprogramming efficiency is generally measured by counting the number of reprogrammed colonies that emerge and grow as rounded clusters of compact cells around 20 days after adding the reprogramming vectors. However, counting colonies manually is labor-intensive, time-consuming, and error-prone.This work develops a semi-automated tool for colony counting from iPSC culture plate images, where colonies are automatically annotated with their centers. Our model uses multi-task learning to jointly predict the colony centers and conduct colony segmentation, in hope that the latter will improve the performance of the former. An annotation tool is developed to facilitate the collection of ground-truth masks by crowdsourcing. Two center-based loss functions are investigated and compared, one based on oriented Gaussian kernel and the other based on average Hausdorff distance. Extensive experiments verify that (i) the former loss outperforms the latter, (ii) the segmentation head is effective in improving center predictions. Our code has been released at

Original languageEnglish
Title of host publicationProceedings - 22nd IEEE International Conference on Data Mining, ICDM 2022
EditorsXingquan Zhu, Sanjay Ranka, My T. Thai, Takashi Washio, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665450997
StatePublished - 2022
Event22nd IEEE International Conference on Data Mining, ICDM 2022 - Orlando, United States
Duration: 28 Nov 20221 Dec 2022

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference22nd IEEE International Conference on Data Mining, ICDM 2022
Country/TerritoryUnited States


  • center
  • counting
  • Gaussian kernel
  • iPSC


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