Impact of Segmentation Quality on Assay Endpoints - Deep Learning for High Content Screening

As High Content Analysis (HCA) has matured and gained wider adoption as a quantitative research tool, the application space has grown and is no longer limited to a finite list of well defined assays performed in standard biological models. To account for this added complexity, a large focus has been placed on improving the flexibility and performance of analysis methods. Machine Learning is becoming ubiquitous and there are many examples these methods outperforming traditional methods for applications across many industries, scientific disciplines, and every day life. One would assume that technologies such as Deep Learning could provide similar benefits in HCA. But with any new technology, one has to weigh the potential benefits against the associated costs of adoption. We will present background information describing some of the fundamental differences between image analysis methods, considerations one should keep in mind when evaluating analysis methods, and some examples suggesting the improvements one could realize by employing technologies such as Deep Learning.

Learning Objectives

• Limitations of image processing methods and the advantages of machine learning for object segmentation
• Survey of machine learning approaches and key differences with respect to their application in HCA
• The impact of improved segmentation accuracy on the overall endpoint of High Content screens