Communication Dans Un Congrès Année : 2023

Transformers-Based Neural Network for Cardiac Infarction Segmentation in Delayed-Enhancement MRI

Résumé

Accurately and robustly segmenting myocardial infarction (MI) is crucial for clinical diagnosis of cardiac diseases, treatment and planning. In this study, we propose a novel deep learning model specifically designed for automatic segmentation of MI in Late Gadolinium Enhancement cardiac MRI (LGE-MRI). LGE-MRI is widely used in clinical practice to quantify MI and plays a vital role in treatment decisions. However, due to the presence of high anisotropy and inhomogeneities in LGE-MRI, accurately segmenting the infarcted tissue poses significant challenges.

Our approach introduces the use of U-Net transformers for MI segmentation. By leveraging the power of transformerbased architectures, our model achieves competitive results. We evaluated our method on the 2020 MICCAI EMIDEC challenge dataset and obtained a dice score of 91.33% for myocardium segmentation and 74.41% for infarction segmentation. These results demonstrate the effectiveness of our approach, showcasing its superiority over existing state-of-the-art methods in the EMIDEC challenge.

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Dates et versions

hal-04945003 , version 1 (17-02-2025)

Identifiants

Citer

Erwan Lecesne, Antoine Simon, Mireille Garreau, Gilles Barone-Rochette, Céline Fouard. Transformers-Based Neural Network for Cardiac Infarction Segmentation in Delayed-Enhancement MRI. 2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA), Oct 2023, Paris, France. pp.1-5, ⟨10.1109/IPTA59101.2023.10320037⟩. ⟨hal-04945003⟩
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