[2211.00750] Automatic Quantitative Analysis of Brain Organoids via Deep Learning
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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2211.00750 (eess)
[Submitted on 1 Nov 2022]

Title:Automatic Quantitative Analysis of Brain Organoids via Deep Learning

Authors:Jingli Shi
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Abstract:Recent advances in brain organoid technology are exciting new ways, which have the potential to change the way how doctors and researchers understand and treat cerebral diseases. Despite the remarkable use of brain organoids derived from human stem cells in new drug testing, disease modeling, and scientific research, it is still heavily time-consuming work to observe and analyze the internal structure, cells, and neural inside the organoid by humans, specifically no standard quantitative analysis method combined growing AI technology for brain organoid. In this paper, an automated computer-assisted analysis method is proposed for brain organoid slice channels tagged with different fluorescent. We applied the method on two channels of two group microscopy images and the experiment result shows an obvious difference between Wild Type and Mutant Type cerebral organoids.
Subjects: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
Cite as: arXiv:2211.00750 [eess.IV]
  (or arXiv:2211.00750v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2211.00750

Submission history

From: Jingli Shi [view email]
[v1] Tue, 1 Nov 2022 21:10:28 UTC (365 KB)
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