Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was created from scratch using the TensorFlow Keras package and trained on those images to categorize them into five distinct replication patterns. Using a holdout test set our model was able to achieve an accuracy of 86.5%. This analysis method for segmentation and classification enhances the efficiency and reproducibility of DNA replication analysis, allowing for high-throughput processing and analysis of replication foci. This research can enhance image analysis in cell biology by providing a time-efficient and accurate tool to investigate replication dynamics, advance cancer research, and contribute to scientific discovery in various domains.
Boyd, Kevin A.; Mitra, Rudranil; Santerre, John; and Sansam, Christopher L.
"Deep Learning Image Analysis to Isolate and Characterize Different Stages of S-phase in Human Cells,"
SMU Data Science Review: Vol. 7:
3, Article 7.
Available at: https://scholar.smu.edu/datasciencereview/vol7/iss3/7
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