High-throughput microscopy enables researchers to acquire thousands of images automatically over a matter of hours. This makes it possible to conduct large-scale, image-based experiments for biological discovery. The main challenge and bottleneck in such experiments is the conversion of “big visual data” into interpretable information and hence discoveries. Visual analysis of large-scale image data is a daunting task. Cells need to be located and their phenotype (e.g., shape) described. The behaviors of cell components, cells, or groups of cells need to be analyzed. The cell lineage needs to be traced. Not only do computers have more “stamina” than human annotators for such tasks, they also perform analysis that is more reproducible and less subjective. The post-acquisition component of high-throughput microscopy experiments calls for effective and efficient computer vision techniques.

This workshop intends to draw more visibility and interest to this challenging yet fruitful field, and establish a platform to foster in-depth idea exchange and collaboration. Authors are invited to submit original and innovative papers. We aim for broad scope, topics of interest include but are not limited to:

·Image acquisition

·Image calibration

·Background correction

·Object detection

·Segmentation

·Stitching and Registration

·Event detection

·Object tracking

·Shape analysis

·Texture analysis

·Classification

·Big image data to knowledge

·Image datasets and benchmarking