Call for papers: Bioimage Computing @ ECCV 2018

Call for papers: Bioimage Computing @ ECCV 2018

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.

Paper Submission: 2. Jul. 2018
Decision Notifications: 6. Aug. 2018
Camera Ready Deadline: After the workshop
Workshop: 14. Sep. 2018

Relevance to the computer vision community. This workshop will bring the latest challenges in bio-image computing to the computer vision community. At the same time it will showcase the specificities of bio-image computing and its current achievements, including issues related to image modeling, denoising, super-resolution, multi-scale segmentation, motion estimation, image registration, tracking, classification, event detection — important topics that 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 bioimaging
3D registration
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)
Creating large collections of labeled training datasets
other technically interesting and biologically useful pipelines and algorithms…

Jens Rittscher (University of Oxford, UK)
Florian Jug (CSBD / MPI-CBG)
Anna Kreshuk (HCI, University Heidelberg / EMBL)

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