@inproceedings{bafc4069de734c06881917fba1827539,
title = "Center-Based iPSC Colony Counting with Multi-Task Learning",
abstract = "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 https://github.com/MTSami/iPSC-Colony-Counting.",
keywords = "center, counting, Gaussian kernel, iPSC",
author = "Sami, {Mirza Tanzim} and Da Yan and Joy, {Bhadhan Roy} and Jalal Khalil and Ricardo Cevallos and Hossain, {Md Emon} and Kejin Hu and Yang Zhou",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 22nd IEEE International Conference on Data Mining, ICDM 2022 ; Conference date: 28-11-2022 Through 01-12-2022",
year = "2022",
doi = "10.1109/ICDM54844.2022.00150",
language = "English",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1173--1178",
editor = "Xingquan Zhu and Sanjay Ranka and Thai, {My T.} and Takashi Washio and Xindong Wu",
booktitle = "Proceedings - 22nd IEEE International Conference on Data Mining, ICDM 2022",
}