Abstract
Purpose: To develop and evaluate a deep learning-based method that allows to perform myocardial infarct segmentation in a fully-automated way. Materials and Methods: For this retrospective study, a cascaded framework of two and three-dimensional convolutional neural networks (CNNs), specialized on identifying ischemic myocardial scars on late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) images, was trained on an in-house training dataset consisting of 144 examinations. On a separate test dataset from the same institution, including images from 152 examinations obtained between 2021 and 2023, a quantitative comparison between artificial intelligence (AI)-based segmentations and manual segmentations was performed. Further, qualitative assessment of segmentation accuracy was evaluated for both human and AI-generated contours by two CMR experts in a blinded experiment. Results: Excellent agreement could be found between manually and automatically calculated infarct volumes ($\rho_c$ = 0.9). The qualitative evaluation showed that compared to human-based measurements, the experts rated the AI-based segmentations to better represent the actual extent of infarction significantly (p < 0.001) more often (33.4% AI, 25.1% human, 41.5% equal). On the contrary, for segmentation of microvascular obstruction (MVO), manual measurements were still preferred (11.3% AI, 55.6% human, 33.1% equal). Conclusion: This fully-automated segmentation pipeline enables CMR infarct size to be calculated in a very short time and without requiring any pre-processing of the input images while matching the segmentation quality of trained human observers. In a blinded experiment, experts preferred automated infarct segmentations more often than manual segmentations, paving the way for a potential clinical application.
Abstract (translated)
目的：开发并评估一种基于深度学习的方法，用于全自动的心肌梗死分割。 材料和方法：在这项回顾性研究中，研究人员使用了一个由两家医院内部数据集训练的级联框架（包括二维和三维卷积神经网络），该框架专门针对在延迟钆增强心脏磁共振成像（LGE CMR）图像上识别心肌缺血疤痕进行优化。训练数据集包含144个检查案例。在一个独立的数据集中进行了定量比较，这个测试数据集来自同一个机构，包括2021年至2023年间获取的152张图像。此外，在一个双盲实验中，由两位CMR专家对人类和AI生成轮廓的分割准确性的定性评估进行了评价。 结果：手动计算的心肌梗死体积与自动计算的结果之间具有很好的一致性（$ρ_c$ = 0.9）。在定性评价方面，专家们认为AI基础的分割比基于人的测量更精确地代表了心肌梗塞的实际范围，这一结论显著得多（p < 0.001），具体来说，33.4%的情况是AI优于人类，25.1%的情况是人工优于AI，而两者相等的情况下占41.5%。然而，在微血管障碍（MVO）的分割方面，手动测量仍然更受欢迎（分别为11.3%，55.6%和33.1%）。 结论：这种全自动的心肌梗死分割管道可以在极短的时间内计算CMR心肌梗死大小，并且无需对输入图像进行任何预处理，同时与训练有素的人类观察者的分割质量相匹配。在双盲实验中，专家们更倾向于选择自动化的梗死分割结果而非手动的分割结果，这为潜在的临床应用铺平了道路。
URL
https://arxiv.org/abs/2502.03272
