Quantification of variegated Drosophila ommatidia with high-resolution image analysis and machine learning.

Abstract

A longstanding challenge in biology is accurately analyzing images acquired using microscopy. Recently, machine learning (ML) approaches have facilitated detailed quantification of images that were refractile to traditional computation methods. Here, we detail a method for measuring pigments in the complex-mosaic adult Drosophila eye using high-resolution photographs and the pixel classifier ilastik [1]. We compare our results to analyses focused on pigment biochemistry and subjective interpretation, demonstrating general overlap, while highlighting the inverse relationship between accuracy and high-throughput capability of each approach. Notably, no coding experience is necessary for image analysis and pigment quantification. When considering time, resolution, and accuracy, our view is that ML-based image analysis is the preferred method.