Enhancing Live-Cell Segmentation in Ptychograpgy Imaging: A Multi-Stage Framework for Error Correction

Poster presented as part of the Crick BioImage Analysis Symposium 2023.

In bioimage analysis, precise visualization of live cellular processes is reliant on accurate segmentations. Ptychography, a label-free technique, provides high-resolution images with minimal interference, yet accurate live-cell segmentation remains difficult due to noise and morphology variations.

Our research introduces a multi-stage framework using machine learning for error-correcting live-cell segmentation in ptychography imaging. By combining classification and semantic segmentation, our approach improves accuracy, offering deeper insights into live-cell behaviour. This poster outlines the innocative framework and its promising results in cell lineage tracking.

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