提出两种新网络，精准分割心脏核磁共振图像中的左心室。
Two Deep Learning Approaches for Automated Segmentation of Left Ventricle in Cine Cardiac MRI
- 用层归一化和实例-批量归一化改进U-Net结构，提升分割精度。
- 在805张图像上测试，骰子系数和垂直距离优于现有方法。
- 适合心脏病影像分析、医学图像分割研究者参考。
左心室分割对心脏影像的临床量化与诊断至关重要。本文提出两种新型深度学习架构——LNU-Net与IBU-Net，用于短轴动态心脏MRI图像中的左心室分割。LNU-Net基于层归一化U-Net，IBU-Net则采用实例-批量归一化设计。二者均包含下采样路径提取特征、上采样路径实现精确定位。以原始U-Net为基准，对比实验表明，所提方法在805张来自45名患者的左心室MRI图像数据集上，实现了更高的骰子系数与更小的平均垂直距离，优于当前主流方法。模型融合仿射变换与弹性形变进行图像增强。
原文摘要 · Abstract (English)
Left ventricle (LV) segmentation is critical for clinical quantification and diagnosis of cardiac images. In this work, we propose two novel deep learning architectures called LNU-Net and IBU-Net for left ventricle segmentation from short-axis cine MRI images. LNU-Net is derived from layer normalization (LN) U-Net architecture, while IBU-Net is derived from the instance-batch normalized (IB) U-Net for medical image segmentation. The architectures of LNU-Net and IBU-Net have a down-sampling path for feature extraction and an up-sampling path for precise localization. We use the original U-Net as the basic segmentation approach and compared it with our proposed architectures. Both LNU-Net and IBU-Net have left ventricle segmentation methods: LNU-Net applies layer normalization in each convolutional block, while IBU-Net incorporates instance and batch normalization together in the first convolutional block and passes its result to the next layer. Our method incorporates affine transformations and elastic deformations for image data processing. Our dataset that contains 805 MRI images regarding the left ventricle from 45 patients is used for evaluation. We experimentally evaluate the results of the proposed approaches outperforming the dice coefficient and the average perpendicular distance than other state-of-the-art approaches.
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