Augmentation Technique For Artificial Phase-Contrast Microscopy Images Generation For The Training Of Deep Learning Algorithms

Abstract

Phase contrast segmentation is crucial for various biological tasks such us quantitative, comparative or single cell level analysis. The popularity of image segmentation using deep learning strategies has been transferred into the field of microscopy imaging as well. Since the huge amount of training data is usually required, the annotation is time-consuming and lengthy. This paper introduces the method and augmentation techniques for artificial phase-contrast images generation aiming at the training of deep learning algorithms.