Publikation
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.
Zusammenfassung
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.