Deep learning-driven automatic nuclei segmentation of live-cell chromatin-sensitive partial wave spectroscopic microscopy imaging

Chromatin-sensitive partial wave spectroscopic (csPWS) microscopy offers a non-invasive glimpse into the mass density distribution of cellular structures at the nanoscale, leveraging the spectroscopic information. Such capability allows us to analyze the chromatin structure and organization and the global transcriptional state of the cell nuclei for the study of its role in carcinogenesis. Accurate segmentation of the nuclei in csPWS microscopy images is an essential step in isolating them for further analysis. However, manual segmentation is error-prone, biased, time-consuming, and laborious, resulting in disrupted nuclear boundaries with partial or over-segmentation. Here, we present an innovative deep-learning-driven approach to automate the accurate nuclei segmentation of label-free (without any exogenous fluorescent staining) live cell csPWS microscopy imaging data. Our approach, csPWS-seg, harnesses the convolutional neural networks-based U-Net model with an attention mechanism to automate the accura...