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

Abstract excerpt

Accurate subcellular segmentation is crucial for understanding cellular processes, but traditional methods struggle with noise and complex structures. Convolutional neural networks improve accuracy but require large, time-consuming, and biased manually annotated datasets. We developed SynSeg, a pipeline generating synthetic training data for a U-Net model to segment subcellular structures, eliminating manual...

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