论文标题
来自多个心脏MRI序列的正常，梗塞和水肿区域的完全自动化深度学习的分割
Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences
论文作者
论文摘要
心肌表征对于心肌梗塞和其他心肌疾病的患者至关重要，并且经常使用心脏磁共振（CMR）序列进行评估。在这项研究中，我们建议使用深卷积神经网络（CNN）进行全自动方法进行心脏病理分割，包括左心室（LV）血池，右心室血池，LV正常心肌，LV心肌表达EMA（ME）和LV心肌障碍（MS）。网络的输入包括三个CMR序列，即晚期增强（LGE），T2和平衡的稳态无稳态预动力（BSSFP）。所提出的方法利用了Miccai 2020与Stacom结合使用的Myops挑战提供的数据。 CNN模型的训练集由从25个病例中获取的图像组成，金标准标签由训练有素的评估者提供，并由放射线医生验证。提出的方法介绍了数据增强模块，线性编码器和解码器模块以及网络模块，以增加训练样本的数量并提高LV ME和MS的预测准确性。提出的方法由挑战组织者评估，其中包括20个案例，并获得LV MS的平均骰子分数为$ 46.8 \％\％$，$ 55.7 \％\％\％\％$ $ $ $ $ $ $ $ $
Myocardial characterization is essential for patients with myocardial infarction and other myocardial diseases, and the assessment is often performed using cardiac magnetic resonance (CMR) sequences. In this study, we propose a fully automated approach using deep convolutional neural networks (CNN) for cardiac pathology segmentation, including left ventricular (LV) blood pool, right ventricular blood pool, LV normal myocardium, LV myocardial edema (ME) and LV myocardial scars (MS). The input to the network consists of three CMR sequences, namely, late gadolinium enhancement (LGE), T2 and balanced steady state free precession (bSSFP). The proposed approach utilized the data provided by the MyoPS challenge hosted by MICCAI 2020 in conjunction with STACOM. The training set for the CNN model consists of images acquired from 25 cases, and the gold standard labels are provided by trained raters and validated by radiologists. The proposed approach introduces a data augmentation module, linear encoder and decoder module and a network module to increase the number of training samples and improve the prediction accuracy for LV ME and MS. The proposed approach is evaluated by the challenge organizers with a test set including 20 cases and achieves a mean dice score of $46.8\%$ for LV MS and $55.7\%$ for LV ME+MS
