Call for Papers

There is an increasing interest in enabling discoveries from high-throughput microscopy imaging of cells and tissues. While automated microscopy acquires thousands of images over a matter of hours, there is a gap in discovery tools to support advanced measurements and decisions. These measurements, decisions and discoveries are critical for developing high quality stem cell therapies, solutions for regenerative medicine, and methodologies for precision medicine. The main challenge and bottleneck in such experiments is the conversion of 'big visual data' and 'heterogeneous sensory data' into interpretable information and hence discoveries and decisions. Visual analysis of large-scale image data is a daunting task. Cells need to be located and tracked over time, and their phenotype has to be characterized by intensity, shape, texture, and motion image measurements. The governing mechanisms of cells have to be analyzed and discovered at sub-cellular, cellular, cell colony, cell sheet, and cell tissue levels. One of the key advantages of computers and automated microscopy analyses is that computers perform analysis more reproducibly and less subjectively than human annotators. Furthermore, high-throughput microscopy experiments call for effective and efficient computer vision techniques since there are not enough human resources to advance science by human annotators.

This workshop intends to bring together computer vision experts from academia, industry, and government who have made progress in developing computer vision tools for microscopy image analysis. It is focused primarily on analyzing microscopy images of samples and phenomena related to human health. It will provide a comprehensive forum on this topic and foster in-depth discussion of technical and application issues. It will also serve as an introduction to researchers and students curious about this important and fertile field.

Authors are invited to submit original and innovative papers. The topics of interest include but are not limited to:

  • Image calibration
  • Background correction
  • Object detection
  • Segmentation
  • Stitching and Registration
  • Event detection
  • Object tracking
  • Shape analysis
  • Texture analysis
  • GPU-based acceleration of image processing
  • Big image data to knowledge
  • Image datasets and benchmarking