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Article Dans Une Revue Computer Methods and Programs in Biomedicine Année : 2023

Segmentation of cardiac infarction in delayed-enhancement MRI using probability map and transformers-based neural networks

Résumé

BACKGROUND AND OBJECTIVE: Automatic segmentation of myocardial infarction is of great clinical interest for the quantitative evaluation of myocardial infarction (MI). Late Gadolinium Enhancement cardiac MRI (LGE-MRI) is commonly used in clinical practice to quantify MI, which is crucial for clinical diagnosis and treatment of cardiac diseases. However, the segmentation of infarcted tissue in LGE-MRI is highly challenging due to its high anisotropy and inhomogeneities. METHODS: The innovative aspect of our work lies in the utilization of a probability map of the healthy myocardium to guide the localization of infarction, as well as the combination of 2D U-Net and U-Net transformers to achieve the final segmentation. Instead of employing a binary segmentation map, we propose using a probability map of the normal myocardium, obtained through a dedicated 2D U-Net. To leverage spatial information, we employ a U-Net transformers network where we incorporate the probability map into the original image as an additional input. Then, To address the limitations of U-Net in segmenting accurately the contours, we introduce an adapted loss function. RESULTS: Our method has been evaluated on the 2020 MICCAI EMIDEC challenge dataset, yielding competitive results. Specifically, we achieved a Dice score of 92.94% for the myocardium and 92.36% for the infarction. These outcomes highlight the competitiveness of our approach. CONCLUSION: In the case of the infarction class, our proposed method outperforms state-of-the-art techniques across all metrics evaluated in the challenge, establishing its superior performance in infarction segmentation. This study further reinforces the importance of integrating a contour loss into the segmentation process.
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Dates et versions

hal-04279117 , version 1 (18-12-2023)

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Erwan Lecesne, Antoine Simon, Mireille Garreau, Gilles Barone-Rochette, Céline Fouard. Segmentation of cardiac infarction in delayed-enhancement MRI using probability map and transformers-based neural networks. Computer Methods and Programs in Biomedicine, 2023, 242, pp.107841. ⟨10.1016/j.cmpb.2023.107841⟩. ⟨hal-04279117⟩
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