A semi-automated algorithm for image analysis of respiratory organoids.

Respiratory organoids have emerged as a powerful in vitro model for studying respiratory diseases and drug discovery. However, the high-throughput analysis of organoid images remains a challenge due to the lack of automated and accurate segmentation tools. This study presents a semi-automatic algorithm for image analysis of respiratory organoids (nasal and lung organoids), employing the U-Net architecture and CellProfiler for organoids segmentation. The algorithm processes bright-field images acquired through z-stack fusion and stitching. The model demonstrated a high level of accuracy, as evidenced by an intersection-over-union metric (IoU) of 0.8856, F1-score = 0.937 and an accuracy of 0.9953. Applied to forskolin-induced swelling assays of lung organoids, the algorithm successfully quantified functional differences in Cystic Fibrosis Transmembrane conductance Regulator (CFTR)-channel activity between healthy donor and cystic fibrosis patient-derived organoids, without fluorescent dyes. Additionally, an open-source dataset of 827 annotated respiratory organoid images was provided to facilitate further research. Our results demonstrate the potential of deep learning to enhance the efficiency and accuracy of high-throughput respiratory organoid analysis for future therapeutic screening applications.