Simulation and Synthesis in Medical Imaging

10th International Workshop, SASHIMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings
Herausgegeben:Fernandez, Virginia; Wiesner, David; Zuo, Lianrui; Casamitjana, Adrià; Remedios, Samuel W.

• Broschiertes Buch

Produktbeschreibung

This book constitutes the refereed proceedings of the 10th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2025, held in conjunction with the 28th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, in Daejeon, South Korea, on September 23, 2025.

The 17 papers included in this book were carefully reviewed and selected from 25 submissions. They focus on wide range of topics relevant to SASHIMI and reflect recent developments in methods for image-to-image translation, generative modelling, physics-inspired synthesis, super-resolution and image segmentation and classification.Applications include magnetic resonance imaging (MRI), computed tomography (CT), optical coherence tomography (OCT) and histopathology imaging.

Produktdetails

• Lecture Notes in Computer Science 16085
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 89568391, 978-3-032-05572-9
• Seitenzahl: 196
• Erscheinungstermin: 21. September 2025
• Englisch
• Abmessung: 235mm x 155mm x 11mm
• Gewicht: 306g
• ISBN-13: 9783032055729
• ISBN-10: 3032055725
• Artikelnr.: 75134608

Inhaltsangabe

.- MedLoRD: A Medical Low-Resource Diffusion Model for High-Resolution 3D CT Image Synthesis.
.- GLFC: Unified Global-Local Feature and Contrast Learning with Mamba-Enhanced
UNet for Synthetic CT Generation from CBCT.
.- 2D to 3D MR Image Super-Resolution using Cross-Contrast Guidance.
.- 3D Super-Resolution for Enhancing Compression Fracture Detection in Thick Slice CT: Diffusion Models vs GANs.
.- From Tissue-Mimicking Phantoms to Physics-Based Scans: Synthetic OCT for
Few-Shot Foundation Model Training.
.- Multi-modal Brain MRI Synthesis with nnU-Net: Exploring Segmentation Per formance and Cross-Modality Relationships.
.- Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning.
.- From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collima tors via Hough Transform.
.- VIOLET: A framework for combined Volumetric Image registration via Opti mization and Learning for Efficient image Translation.
.- Generation of Controllable and Photorealistic Synthetic Cataract Surgery Im ages: Blending 3D Models and Real-World Data.
.- Unsupervised MRI Harmonization via Parameter Prediction and Super-Resolved
MPMs.
.- Learning Mechanistic Subtypes of Neurodegeneration with a Physics-Informed
Variational Autoencoder Mixture Model.
.- FastDTI: A 3D Scale-arbitrary Super-resolution Autoencoder Residual Dense
Network for DTI.
.- Lesion-Aware CT-to-MRI Synthesis using a Mask-Informed Diffusion with Adaptive Weighted Loss (MIDAS).
.-Conditional Iterative -(de)Blending Model for CBCT-tosCT Synthesis: Towards a Deterministic and Simple Process.
.- Synthesizing Accurate and Realistic T1-weighted Contrast-Enhanced MR Images using Posterior-Mean Rectified Flow.
.- Clustering-based Stain Augmentation: Templates for Periodic Acid-Schiff Biopsy
Images.