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Cardiac MR segmentation based on sequence propagation by deep learning.
基于深度学习序列传播的心脏MR分割 [j].
- 影响因子:3.02
- DOI:10.1371/journal.pone.0230415
- 作者列表:"Luo C","Shi C","Li X","Gao D
- 发表时间:2020-04-09
Abstract
:Accurate segmentation of myocardial in cardiac MRI (magnetic resonance image) is key to effective rapid diagnosis and quantitative pathology analysis. However, a low-quality CMR (cardiac magnetic resonance) image with a large amount of noise makes it extremely difficult to accurately and quickly manually segment the myocardial. In this paper, we propose a method for CMR segmentation based on U-Net and combined with image sequence information. The method can effectively segment from the top slice to the bottom slice of the CMR. During training, each input slice depends on the slice below it. In other words, the predicted segmentation result depends on the existing segmentation label of the previous slice. 3D sequence information is fully utilized. Our method was validated on the ACDC dataset, which included CMR images of 100 patients (1700 2D MRI). Experimental results show that our method can segment the myocardial quickly and efficiently and is better than the current state-of-the-art methods. When evaluating 340 CMR image, our model yielded an average dice score of 85.02 ± 0.15, which is much higher than the existing classical segmentation method(Unet, Dice score = 0.78 ± 0.3).
摘要
: 心脏MRI (磁共振图像) 中心肌的准确分割是有效快速诊断和定量病理分析的关键。然而,具有大量噪声的低质量CMR (心脏磁共振) 图像使得精确和快速地手动分割心肌极其困难。本文提出了一种基于U-Net并结合图像序列信息的CMR分割方法。该方法可以有效地从CMR的顶部切片分割到底部切片。在训练期间,每个输入切片取决于它下面的切片。换句话说,预测的分割结果取决于先前切片的现有分割标签。充分利用了3D序列信息。我们的方法在ACDC数据集上得到验证,其中包括100例患者的CMR图像 (1700 2D MRI)。实验结果表明,我们的方法可以快速有效地分割心肌,优于目前最先进的方法。在评估340幅CMR图像时,我们的模型得到的dice平均得分为85.02 ± 0.15,远高于现有的经典分割方法 (Unet,Dice得分 = 0.78 ± 0.3)。
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心脏结构和心脏血流的可视化,用于诊断评估或通过内窥镜、放射性核素成像等技术来指导心脏手术。