论文标题
心脏磁共振图像中心脏壁分割的时间外推通过像素跟踪
Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking
论文作者
论文摘要
在这项研究中，我们为心脏磁共振图像中心室分割掩模的时间外推定了一种像素跟踪方法。像素跟踪过程从心脏周期的末期框架开始，使用可用的手动分割图像来预测终端节感应分段掩码。 Superpixels方法用于将原始图像分为较小的单元格，在每个时间范围内，将新标签分配给图像单元，从而导致跟踪通过不同框架的心脏壁元素的运动。将收缩期末端的履带掩膜与已经可用的手动分割面罩进行了比较，并且发现骰子得分在0.81至0.84之间。考虑到提出的方法不一定需要培训数据集这一事实，在训练数据受到限制的情况下，这可能是一种有吸引力的深度学习分割方法的替代方法。
In this study, we have tailored a pixel tracking method for temporal extrapolation of the ventricular segmentation masks in cardiac magnetic resonance images. The pixel tracking process starts from the end-diastolic frame of the heart cycle using the available manually segmented images to predict the end-systolic segmentation mask. The superpixels approach is used to divide the raw images into smaller cells and in each time frame, new labels are assigned to the image cells which leads to tracking the movement of the heart wall elements through different frames. The tracked masks at the end of systole are compared with the already available manually segmented masks and dice scores are found to be between 0.81 to 0.84. Considering the fact that the proposed method does not necessarily require a training dataset, it could be an attractive alternative approach to deep learning segmentation methods in scenarios where training data are limited.
