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

Abstract

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 model, which when compared to manual masks, achieved mean Dice scores >0.8 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 127x 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.