An automated image analysis pipeline for wide-field optical redox imaging of patient-derived cancer organoids

Wide-field optical redox imaging provides a fast and accessible method to monitor metabolic changes in cells and has recently been developed for drug screening in patient-derived cancer organoids (PDCOs). However, manual analysis of wide-field optical redox images is inefficient and laborious for large-scale drug screens. Here, we developed an automated pipeline for PDCO segmentation, single-PDCO tracking, and background correction in autofluorescence images. This pipeline was tested on two imaging systems over a 3-day time-course with two drug doses to demonstrate generalizability across imaging systems. Segmentation was performed using a fine-tuned Cellpose 3 model, which when compared to manual masks, outperformed Cellpose-SAM (4) and achieved high dice and recall scores across systems, indicating high reproducibility. Automated single-PDCO tracking was compared to manual tracking and the accuracy of the tracking algorithm exceeded 94% by two metrics, recall and Jaccard index. For background correction, the automated pipeline uses the full field-of-view to reduce sampling bias. Compared to the manual analysis pipeline, the automated pipeline resolves single-PDCO responses with comparable sensitivity to drug treatment but with over 127× faster processing time. This novel automated image analysis pipeline improves throughput and robustness in PDCO image analysis, which increases the accessibility and scalability of wide-field optical redox imaging for PDCO drug screening.