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Publication

CellGenie: An end-to-end Pipeline for Synthetic Cellular Data Generation and Segmentation: A Use Case for Cell Segmentation in Microscopic Images

Nabeel Khalid; Mohammadmahdi Koochali; Duway Nicolas Lesmes Leon; Maria Caroprese; Gillian Lovell; Daniel A Porto; Johan Trygg; Andreas Dengel; Sheraz Ahmed
In: 28th UK Conference on Medical Image Understanding and Analysis - MIUA. Medical Image Understanding and Analysis (MIUA-2024), Spriner Nature, 8/2024.

Abstract

Cellular imaging plays a pivotal role in understanding various biological processes and diseases, making accurate cell segmentation indispensable for many biomedical applications. However, traditional methods for cell segmentation often rely on manual annotation, which is labor-intensive and time-consuming. Deep learning-based approaches for cell segmentation have shown promising results, but they require a vast amount of annotated data for training. In this context, this study presents CellGenie, an end-to-end pipeline designed to address the challenge of data scarcity in deep learning-based cell segmentation. This research proposes an innovative approach for automatic synthetic data generation tailored for microscopic image analysis. Leveraging the rich information provided by the LIVECell dataset, CellGenie generates synthetic microscopic images along with their corresponding segmentation masks for individual cells. By seamlessly integrating this synthetic data into the training process, this study enhances the performance of cell segmentation models beyond the limitations of existing annotated dataset. Furthermore, extensive experimentations are conducted to evaluate the efficacy of the generated data across various experimental scenarios. The results demonstrate the substantial impact of synthetic data generation in improving the robustness and generalization of cell segmentation models.

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