Accepted Papers

Regular papers

Important information:
  • All accepted papers must be presented as a poster during the workshop.
  • Some papers are selected for an oral presentation: 🗣️
  • Poster format must follow the same MICCAI guideline about poster presentation.

  1. 🗣️ Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation
    Rygiel, P., Suk, J., Yeung, K., Brune, C., and Wolterink, J.

  2. 🗣️ LGESynthNet: Controlled Scar Synthesis for Improved Scar Segmentation in Cardiac LGE-MRI Imaging
    Jacob, A., Sharma, P., and Rueckert, D.

  3. 🗣️ A comprehensive Pipeline for Aortic Segmentation and Shape Analysis
    Shehata, N., Elsawy, A., Nagy, M., ElMahdy, M., Ali, M., Romeih, S., Aguib, H., Yacoub, M., and Glocker, B.

  4. 🗣️ Fast multi-label parameterization of the left atrium by learned template morphing
    Castaneda, E., Raghunath, A., Meister, F., Weiss, M., Mihalef, V., Ashikaga, H., Maier, A., Passerini, T., and Lluch, È.

  5. 🗣️ Groupwise Registration with Physics-Informed Test-Time Adaptation on Multi-parametric Cardiac MRI
    Li, X., Zhang, Y., Huang, L., Chang, H., Niendorf, T., Ku, M., Tao, Q., and Yang, H.

  6. 🗣️ Comprehensive 4D flow MRI characterization of left atrial hemodynamic flow components in hypertension and hypertrophic cardiomyopathy
    Casademunt, P., Morales, X., Elsayed, A., Zhao, D., Loncaric, F., Quill, G., Ramos, M., Doltra, A., Sitges, M., Lowe, B., Young, A., Nash, M., and Camara, O.

  7. A Clinically-Informed Benchmark for Topology-Aware Coronary Artery Segmentation
    Acebes, C., Galdran, A., Moustafa, A., Clapers, M., and Camara, O.

  8. KP-INR: A Dual-Branch Implicit Neural Representation Model for Cartesian Cardiac Cine MRI Reconstruction
    Lyu, D., Staring, M., Doneva, M., Lamb, H., and Pezzotti, N.

  9. Scalable Automated Framework for Left Atrial Appendage Segmentation, Clustering, and CFD Analysis
    Raghunath, A., Castaneda, E., Jacob, A., Weiss, M., Passerini, T., Mihalef, V., Voigt, I., Maier, A., and Lluch, È.

  10. Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement
    Zöllner, C., Reiß, S., Jaus, A., Sholi, A., Sodian, R., and Stiefelhagen, R.

  11. Medical SAM for Cardiac Segmentation: Promise or hype?
    Goujat, C., Jodoin, P., Boussel, L., and Bernard, O.

  12. Implicit Neural Representations of Intramyocardial Motion and Strain
    Bell, A., King, A., Choi, Y., Petersen, S., Nazir, S., and Young, A.

  13. openCARP-PINNs
    Zolotarev, A., Ip, S., Vydyula, K., Dhillon, B., Martín, C., Misghina, S., and Roney, C.

  14. Automated CT-derived Fractional Flow Reserve using Vision Transformers and Computational Fluid Dynamics
    Fossan, F., Larsen, M., Bråten, A., Jensen, S., Mogen, S., Jørgensen, A., Hellevik, L., Wiseth, R., Kiss, G., and Lindseth, F.

  15. 3D Reconstruction of Coronary Vessel Trees from Biplanar X-Ray Images Using a Geometric Approach
    Koland, E., Xi, L., Wijesuriya, N., and Ma, Y.

  16. Construction of a Bi-Atrial Statistical Shape Atlas for In-Silico Population Studies
    Misghina, S., Zolotarev, A., Rauseo, E., Slabaugh, G., Aung, N., Munroe, P., Petersen, S., and Roney, C.

  17. Adaptive k-space Radial Sampling for Cardiac MRI with Reinforcement Learning
    Xu, R. and Oksuz, I.

  18. Cardiovascular disease classification using radiomics and geometric features from cardiac CT Images
    Mittal, A., Mehta, R., Todd, O., Seeboeck, P., Langs, G., and Glocker, B.

  19. TASSNet: A Deep Learning Framework for Robust Bi-Atrial Segmentation in 3D LGE-MRI of Atrial Fibrillation
    Gunawardhana, M., Trew, M., Sands, G., and Zhao, J.

  20. Generating Synthetic Contrast-Enhanced Chest CT Images from Non-Contrast Scans Using a Slice-Consistent Bridge Diffusion Network
    Shiri, P., Yi, X., Mistry, N., Javadinia, S., Chegini, M., Ko, S., Baniasadi, A., and Adams, S.

  21. A public cardiac CT dataset featuring the left atrial appendage
    Hansen, B., Pedersen, J., Kofoed, K., Camara, O., Paulsen, R., and Sørensen, K.

  22. Voronoi-based myocardial perfusion region prediction enhanced by GCN
    Srir, M., Sallé de Chou, R., Lynch, S., Talbot, H., Najman, L., Sinclair, M., and Vignon-Clementel, I.

  23. Radiomics for Predicting TAVI-related Complications: A Fully-Automatic, Interpretable Alternative to CNNs and Conventional Anatomical Measurements
    Krüger, N., Brosig, J., Laube, A., Ivantsits, M., Hüllebrand, M., Wamala, I., Khasyanova, I., Kempfert, J., Meyer, A., Sündermann, S., Dreger, H., Falk, V., Kühne, T., and Hennemuth, A.

  24. Relative Influence of Hypercoagulability, Left Atrial Appendage Geometry, and Pulmonary Vein Velocity on Thrombus Formation
    Melidoro, P., Cavarra, R., Klis, M., Lip, G., Williams, S., Aslanidi, O., and De Vecchi, A.

  25. Enhanced Stroke Risk Stratification of Atrial Fibrillation Patients Using Explainable Machine Learning
    Cavarra, R., Ogbomo-Harmitt, S., Melidoro, P., Williams, S., De Vecchi, A., King, A., and Aslanidi, O.

  26. Machine learning highlights left atrial fibrotic heterogeneity as a key predictor of atrial fibrillation inducibility
    Obada, G., Ogbomo-Harmitt, S., Deprez, M., and Aslanidi, O.

  27. Deep Learning-Based Segmentation of 3D Left Atrial Meshes from Electroanatomical Mapping: Left Atrium Mesh Segmentation
    Mancebo-Laguna, I., Alonso P.Á., Sánchez, R.G., Pérez, M.M., Gómez, M.A., Carta-Bergaz, A., Atienza, F., Arenal, Á., and Ríos-Muñoz G.R.