Image Processing

Original papers are invited on all aspects of the processing and analysis of medical, small animal, or cellular images, with applications in medicine, biological, and pharmaceutical research. Of interest are algorithms applied to all imaging modalities, including X-ray, DSA, CT, MRI, neuroimaging, nuclear medicine, optical, ultrasound, macroscopic, and microscopic imaging. Papers dealing with the challenges of bringing advances in research laboratories into clinical application are particularly welcomed.

Papers typically involve research that includes one or more of the following categories (in alphabetical order):

• augmented/virtual reality
• classification
• computational anatomy and atlases
• computer vision for medical imaging
• deep learning
• deformable geometry
• foundation models
• generative models
• image representation and compression
• image restoration and enhancement
• image synthesis
• imaging genetics
• machine learning and pattern recognition
• methods for training and validation, including ground truth generation
• motion/time series analysis
• open software for medical image processing
• population/clinical studies
• quantitative image analysis/quantitative imaging biomarkers
• registration methodologies
• radiomics and texture representation/analysis
• segmentation methodologies
• shape representation and analysis
• statistical methodology
• vision language models
• visualization methods.

TOPIC AREAS: For this conference only
To assist the reviewers, during submission, select up to three keywords in order of relevance from the following list:

• application: brain
• application: cancer
• application: cardiovascular
• application: eye
• augmented/virtual reality
• deep learning: CNN
• deep learning: transformers
• deep learning: graph neural networks
• deep learning: generative models
• deep learning: vision-language models and foundation models
• deformable geometry, shape analysis
• explainable and trustworthy AI
• image reconstruction, representation, restoration and enhancement
• image synthesis and generative models
• integration of imaging and non-imaging data (genetics, omics…)
• longitudinal data, motion/time series analysis
• machine learning and pattern recognition
• methods for training and validation, including ground truth generation
• modality: CT, radiography
• modality: MRI
• modality: nuclear medicine (PET, SPECT…)
• modality: ultrasound
• population/clinical studies and validation studies
• quantitative image analysis/quantitative imaging biomarkers
• registration
• segmentation
• software platform
• statistical methodology
• visualization
• deep-dive session.

Deep-dive session
A limited number of papers selected for oral presentation will be chosen for a novel deep-dive session. This session will comprise very few oral presentations followed by a longer more in-depth discussion that will be led by experienced researchers. In order to be considered for the deep-dive session, during submission of your abstract select "deep-dive session" as one of your topics from the above list of topics.