SPAGHETTI leverages massive H\&E morphological models for phase contrast microscopy images with a generative deep learning approach

Phase contrast microscopy (PCM) is a powerful cell imaging method, one of the few technologies to delineate and track cell structure in live cells without staining. Despite PCMs great potential and popularity in monitoring live-cell populations, there is a lack of algorithms to extract morphological information from these images due to the lack of large training datasets. To overcome this challenge and enable advanced, high throughput quantitative analysis of PCM images, we introduce SPAGHETTI: a lightweight image translator built on a modified cycle-consistent generative adversarial network. SPAGHETTI translates PCM images into those resembling hematoxylin and eosin (H&E) pathological images which, due to the pervasive and widespread use in clinical settings, are the basis for most large-scale deep learning models for quantitative analyses. We demonstrate that by first using SPAGHETTI to translate PCM images into H&E-like images, we could achieve significantly improved performance on cell segmentation through the use of tissue and H&E-specific cell segmentation models. We also show that by passing translated PCM images across several independent datasets into H&E feature extractor models, we improve the performance of cell-type annotation, experimental media classification, and cell viability prediction. Overall, SPAGHETTI enables many quantitative analyses of PCM that were previously impossible and acts as a valuable preprocessing step to help researchers gather novel information about cell states through the downstream quantitative analysis of morphological features.