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This book constitutes the refereed proceedings of the 9th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024.
The 19 papers included in this book were carefully reviewed and selected from 32 submissions. They focus on recent developments in methods for image-to-image translation, image synthesis, biophysical modelling, super-resolution and image segmentation and classification.
List of contents
.- Synthetic Augmentation for Anatomical Landmark Localization using DDPMs.
.- AdaptDiff: Cross-Modality Domain Adaptation via Weak Conditional Semantic Diffusion for Retinal Vessel Segmentation.
.- Adapted nnU-Net: A Robust Baseline for Cross-Modality Synthesis and Medical Image Inpainting.
.- Beyond MR Image Harmonization: Resolution Matters Too.
.- Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data.
.- A dual-task mutual learning framework for predicting post-thrombectomy cerebral haemorrhage.
.- TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification.
.- Beyond Intensity Transforms: Medical Image Synthesis Under Large Deformation.
.- Sim2Real in endoscopy segmentation with a novel structure aware image translation.
.- Fireflies: Photorealistic Simulation and Optimization of Structured Light Endoscopy.
.- Exogenous Agent-Free Synthetic Post-Contrast Imaging with a Cascade of Deep Networks for enhancement Prediction after Tumor Resection. A Parametric-Map Oriented Approach.
.- OCT Scans Simulation Framework for Data Augmentation and Controlled Evaluation of Signal Processing Approaches.
.- Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Image.
.- Latent Pollution Model: The Hidden Carbon Footprint in 3D Image Synthesis.
.- Synthesizing Scalable CFD-Enhanced Aortic 4D Flow MRI for Assessing Accuracy and Precision of Deep-Learning Image Reconstruction and Segmentation Tasks.
.- MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI.
.- Using MR physics for domain generalisation and super-resolution.
.- Single-scan mpMRI Calibration of Multi-Species Brain Tumor Dynamics with Mass Effect.
.- Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields.
