BioImage Computing @ ICCV 2017

Thanks to everyone making this workshop become as interesting and inspiring as it was!

Bio-image computing (BIC) emerges as a rapidly growing field on the interface between engineering, biology and computer science. State-of-the-art light microscopy (LM) can deliver 2D and 3D image sequences of living cells with unprecedented image quality and ever growing resolution in space and time. The emergence of novel and quite diverse LM modalities has provided biologists with formidable means to explore cell mechanisms, embryogenesis, or neural development, to quote just a few fundamental biological issues. Electron microscopy (EM) supplies information on the cell structure down to the nanometer resolution. Correlating LM and EM at the microscopic level, and both with animal behavior at the macroscopic level, is of paramount importance. In the face of huge data sets, at a size of multiple TB per volume or video, and exceedingly difficult problems, state-of-the-art computer vision are required and need to be further developed.

Relevance to the computer vision community. The workshop will bring the latest challenges in bio-image computing to the computer vision community members, while allowing them to know more about the specificities of bio-image computing and its current achievements. This includes important issues related to image modeling, denoising, super-resolution, multi-scale segmentation, motion estimation, image registration, tracking, classification, event detection, topics which appertain to the computer vision field.

Topics of interest include:

  • LM and EM image restoration and reconstruction
  • Reproducible image analyses over terabyte-sized images
  • Deep learning for Bio-imaging
  • 3D registration between bioimage stacks
  • Segmentation of subcellular objects, cells, and animals (instances and classes)
  • Multimodal image analysis (correlative LM and EM, various LM modalities)
  • Analysis of motion: particle tracking and tracking of cells, tissues, and organisms
  • Automated behavior recognition
  • Dense motion estimation in 2D and 3D LM image sequences
  • Diffusion computation
  • Statistical analysis of (object) shape
  • Image-based phenotyping
  • Evaluation and benchmarking methodologies of automated image algorithms/pipelines
  • Interactive image analyses (of gigapixel images)

Previous editions...