Lecesne Erwan, Simon Antoine, Garreau Mireille, Barone-Rochette Gilles, Fouard Céline
Univ Rennes, Inserm, LTSI - UMR 1099, Rennes, 35000, France.
Univ Rennes, Inserm, LTSI - UMR 1099, Rennes, 35000, France.
Comput Methods Programs Biomed. 2023 Dec;242:107841. doi: 10.1016/j.cmpb.2023.107841. Epub 2023 Oct 13.
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.
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.
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.
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.
心肌梗死的自动分割对于心肌梗死（MI）的定量评估具有重要的临床意义。延迟钆增强心脏磁共振成像（LGE-MRI）在临床实践中常用于量化MI，这对心脏病的临床诊断和治疗至关重要。然而，由于LGE-MRI中梗死组织的高度各向异性和不均匀性，其分割极具挑战性。
我们工作的创新之处在于利用健康心肌的概率图来指导梗死灶的定位，并结合二维U-Net和U-Net变换器来实现最终分割。我们不是使用二值分割图，而是提出使用通过专用二维U-Net获得的正常心肌概率图。为了利用空间信息，我们采用U-Net变换器网络，将概率图作为额外输入融入原始图像。然后，为了解决U-Net在精确分割轮廓方面的局限性，我们引入了一种适应性损失函数。
我们的方法在2020年MICCAI EMIDEC挑战赛数据集上进行了评估，取得了具有竞争力的结果。具体而言，我们在心肌分割方面的Dice分数为92.94%，在梗死灶分割方面为92.36%。这些结果突出了我们方法的竞争力。
在梗死灶类别方面，我们提出的方法在挑战赛评估的所有指标上均优于现有技术，证明了其在梗死灶分割方面的卓越性能。本研究进一步强化了在分割过程中整合轮廓损失的重要性。
