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SASHIMI 2026

Eleventh International Workshop on Simulation and Synthesis in Medical Imaging

1790834400000Strasbourg, FranceOfficial workshop site Site reachable

SASHIMI 2026 is a MICCAI-affiliated workshop focused on simulation and synthesis in medical imaging, aiming to advance methods for generating synthetic medical data to improve algorithm development, evaluation, and clinical training. It brings together researchers working on deep learning, mechanistic modeling, and applications of synthetic data in areas such as segmentation, registration, and image reconstruction, with an emphasis on reproducibility and benchmarking.

Official CFP Submission site Back to deadlines Verified September 9, 2026

Paper fit

Contribution paths

A strong submission should clearly identify its contribution and evaluate it appropriately.

Method-oriented paper

The main emphasis during the review process will be put on the novelty, correctness of the presented method, and proper validation of the results. Authors are expected to submit a full-length paper which will be included in the conference proceedings.

Application-oriented paper

The main focus will be put on the novel application of an existing theoretical image synthesis method and the data it is applied to. Detailed description of data collection or sourcing is required. Authors are expected to submit a full-length paper which will be included in the conference proceedings.

Research areas in scope

Fundamental Methods and Models

Fundamental methods for image-based biophysical modeling and image synthesisBiophysical and data-driven models of disease progression, organ development, motion and deformation, image formation and acquisitionVirtual cell imagingDetailed mechanistic models (top–down) incorporating priors on geometry and physics of image acquisitionComplex spatio-temporal computational models of anatomical variability, organ physiology, and morphological changes

Machine and Deep Learning Techniques

Machine and deep learning techniques in image simulation and synthesisDeep learning methods including fully-supervised, semi-supervised, self-supervised, unsupervised, transfer, and multi-task learningDeep learning model architectures including Generative Adversarial Network (GAN), Variational Auto-Encoder (VAE), Flows, TransformersHandling uncertainty and incomplete data via simulation and synthesis techniquesImage synthesis in high dimensional spaces (vectors, tensors, spatio-temporal features, etc.)

Applications

Segmentation/registration across or within modalities to aid the learning of model parametersImaging protocol harmonization approaches across imaging systems, sites and time pointsImage synthesis for normalization and spatio-temporal intensity correctionCross modality (PET/MR, PET/CT, CT/MR, etc.) image synthesisSimulation and synthesis from large-scale databasesApplications of image synthesis in super resolution imaging and multi/cross-scale regressionApplications of image synthesis and simulation in medical image registration and segmentationApplications of synthesis and simulation to image reconstruction from sparse data or sparse viewsApplications of image synthesis in denoising, fusion reconstruction and real-time simulation of biophysical properties

Evaluation and Benchmarking

Automated techniques for quality assessment of simulations and synthetic imagesEvaluation and benchmarking of state of-the-art approaches in simulation and synthesisNormative and annotated datasets for benchmarking and learning modelsNovel ideas on evaluation metrics and methods in image-based simulation and image synthesis

Policies worth checking twice

Papers must be submitted using the MICCAI 2026 LaTeX or MS Word templates with no modifications permitted.
Manuscripts are limited to 8 pages of text, figures, tables, conclusions, and acknowledgments, plus up to 2 pages of references.
The review process is double-blinded; authors must anonymize all content including code repositories and supplementary materials.
Authors must not publish their submissions on arXiv or other platforms prior to acceptance to preserve double-blind review.
Accepted papers must be presented in person by an author registered for on-site participation; failure to present results in withdrawal from proceedings.
Authorship changes are not permitted after camera-ready submission to Springer.
The corresponding author must sign a Licence-to-Publish form, and must match the corresponding author marked on the paper.
Camera-ready papers must include author names and affiliations (no longer anonymous).

Official sources

Compiled from the official call for papers. The organizers’ pages remain authoritative.

Official workshop site Call for papers Important dates Submission Submission page

Last verified September 9, 2026