SynSeg: A synthetic data-driven approach for robust subcellular structure segmentation.

Area of Science:

• Cell Biology
• Bioimaging
• Computational Biology

Background:

• Accurate subcellular segmentation is essential for understanding cellular functions but is hindered by noise and complex cellular structures.
• Traditional segmentation methods and existing deep learning approaches often require extensive manual annotation, which is time-consuming, labor-intensive, and prone to bias.

Purpose of the Study:

• To develop a novel pipeline, SynSeg, for generating synthetic training data to automate subcellular structure segmentation.
• To overcome the limitations of manual annotation in deep learning-based image analysis for cell biology.

Main Methods:

• Developed SynSeg, a pipeline that generates diverse synthetic datasets (varying intensity, morphology, signal distribution) for training U-Net models.
• Applied SynSeg to segment vesicles and cytoskeletal filaments in cellular and live Caenorhabditis elegans images.
• Validated SynSeg's performance against traditional methods (Otsu's, ILEE, FilamentSensor 2.0) and a recent deep learning approach.

Main Results:

• SynSeg achieved superior performance in segmenting subcellular structures, outperforming existing methods.
• The pipeline enabled accurate quantification of disease-associated microtubule morphology in live cells, identifying defects linked to Tau proteins.
• SynSeg facilitated high-throughput analysis, revealing that BSCL2 mutations increase lipid droplet size and demonstrating broad applicability in quantitative cell biology.

Conclusions:

• SynSeg effectively automates subcellular segmentation using synthetic data, eliminating the need for manual annotation.
• The pipeline offers a powerful, generalizable tool for quantitative cell biology, particularly for analyzing challenging image data and disease-related cellular changes.
• Synthetic data generation holds significant potential for advancing automated biological image analysis and segmentation tasks.