PURPOSE Segmentation of magnetic resonance images (MRI) of the left ventricle (LV) plays a key role in quantifying the volumetric functions of the heart, such as the area, volume, and ejection fraction. Traditionally, LV segmentation is performed manually by experienced experts, which is both time-consuming and prone to subjective bias. This study aims to develop a novel capsule-based automated segmentation method to automatically segment the LV from images obtained by cardiac MRI. METHOD The technique applied for segmentation uses Fourier analysis and the circular Hough transform (CHT) to indicate the approximate location of the LV and a network capsule to precisely segment the LV. The neurons of the capsule network output a vector and preserve much of the information about the input by replacing the largest pooling layer with convolutional strides and dynamic routing. Finally, the segmentation result is postprocessed by threshold segmentation and morphological processing to increase the accuracy of LV segmentation. RESULTS We fully exploit the capsule network to achieve the segmentation goal and combine LV detection and capsule concepts to complete LV segmentation. In the experiments, the tested methods achieved LV Dice scores of 0.922±0.05 end-diastolic (ED) and 0.898±0.11 end-systolic (ES) on the ACDC 2017 data set. The experimental results confirm that the algorithm can effectively perform LV segmentation from a cardiac magnetic resonance image. To verify the performance of the proposed method, visual and quantitative comparisons are also performed, which show that the proposed method exhibits improved segmentation accuracy compared with the traditional method. CONCLUSIONS The evaluation metrics of medical image segmentation indicate that the proposed method in combination with postprocessing and feature detection effectively improves segmentation accuracy for cardiac MRI. To the best of our knowledge, this study is the first to use a deep learning model based on capsule networks to systematically evaluate end-to-end LV segmentation.

中文翻译：

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使用胶囊网络从心脏磁共振图像自动分割左心室。

目的 左心室 (LV) 磁共振图像 (MRI) 的分割在量化心脏的容积功能（例如面积、容积和射血分数）方面起着关键作用。传统上，LV 分割是由经验丰富的专家手动执行的，这既耗时又容易产生主观偏见。本研究旨在开发一种新的基于胶囊的自动分割方法，以从心脏 MRI 获得的图像中自动分割 LV。方法 应用于分割的技术使用傅立叶分析和圆形霍夫变换 (CHT) 来指示 LV 的大致位置，并使用网络胶囊精确分割 LV。胶囊网络的神经元输出一个向量，并通过用卷积步幅和动态路由替换最大的池化层来保留有关输入的大部分信息。最后对分割结果进行阈值分割和形态学处理，提高LV分割的准确性。结果我们充分利用胶囊网络来实现分割目标，并结合 LV 检测和胶囊概念来完成 LV 分割。在实验中，测试方法在 ACDC 2017 数据集上实现了 0.922±0.05 舒张末期 (ED) 和 0.898±0.11 收缩末期 (ES) 的 LV Dice 评分。实验结果证实，该算法可以有效地从心脏磁共振图像中进行 LV 分割。为了验证所提出方法的性能，还进行了视觉和定量比较，这表明与传统方法相比，所提出的方法具有更高的分割精度。结论医学图像分割的评价指标表明，所提出的方法结合后处理和特征检测有效地提高了心脏MRI的分割精度。据我们所知，这项研究是第一个使用基于胶囊网络的深度学习模型来系统地评估端到端 LV 分割的研究。结论医学图像分割的评价指标表明，所提出的方法结合后处理和特征检测有效地提高了心脏MRI的分割精度。据我们所知，这项研究是第一个使用基于胶囊网络的深度学习模型来系统地评估端到端 LV 分割的研究。结论医学图像分割的评价指标表明，所提出的方法结合后处理和特征检测有效地提高了心脏MRI的分割精度。据我们所知，这项研究是第一个使用基于胶囊网络的深度学习模型来系统地评估端到端 LV 分割的研究。