Wu Lingeer, Ling Yijun, Lan Ling, He Kai, Yu Chunhua, Zhou Zhuhuang, Shen Le
Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
Diagnostics (Basel). 2024 Dec 9;14(23):2766. doi: 10.3390/diagnostics14232766.
The automatic left ventricle segmentation in transesophageal echocardiography (TEE) is of significant importance. In this paper, we constructed a large-scale TEE apical four-chamber view (A4CV) image dataset and proposed an automatic left ventricular segmentation method for the TEE A4CV based on the UNeXt deep neural network. UNeXt, a variant of U-Net integrating a multilayer perceptron, was employed for left ventricle segmentation in the TEE A4CV because it could yield promising segmentation performance while reducing both the number of network parameters and computational complexity. We also compared the proposed method with U-Net, TransUNet, and Attention U-Net models. Standard TEE A4CV videos were collected from 60 patients undergoing cardiac surgery, from the onset of anesthesia to the conclusion of the procedure. After preprocessing, a dataset comprising 3000 TEE images and their corresponding labels was generated. The dataset was randomly divided into training, validation, and test sets in an 8:1:1 ratio on the patient level. The training and validation sets were used to train the UNeXt, U-Net, TransUNet, and Attention U-Net models for left ventricular segmentation. The dice similarity coefficient (DSC) and Intersection over Union (IoU) were used to evaluate the segmentation performance of each model, and the Kruskal-Wallis test was employed to analyze the significance of DSC differences. On the test set, the UNeXt model achieved an average DSC of 88.60%, outperforming U-Net (87.76%), TransUNet (85.75%, < 0.05), and Attention U-Net (79.98%; < 0.05). Additionally, the UNeXt model had a smaller number of parameters (1.47 million) and floating point operations (2.28 giga) as well as a shorter average inference time per image (141.73 ms), compared to U-Net (185.12 ms), TransUNet (209.08 ms), and Attention U-Net (201.13 ms). The average IoU of UNeXt (77.60%) was also higher than that of U-Net (76.61%), TransUNet (77.35%), and Attention U-Net (68.86%). This study pioneered the construction of a large-scale TEE A4CV dataset and the application of UNeXt to left ventricle segmentation in the TEE A4CV. The proposed method may be used for automatic segmentation of the left ventricle in the TEE A4CV.
经食管超声心动图（TEE）中的左心室自动分割具有重要意义。在本文中，我们构建了一个大规模的TEE心尖四腔心视图（A4CV）图像数据集，并提出了一种基于UNeXt深度神经网络的TEE A4CV左心室自动分割方法。UNeXt是U-Net的一个变体，集成了多层感知器，用于TEE A4CV中的左心室分割，因为它在减少网络参数数量和计算复杂度的同时，能够产生良好的分割性能。我们还将所提出的方法与U-Net、TransUNet和注意力U-Net模型进行了比较。从60例接受心脏手术的患者中收集了标准的TEE A4CV视频，从麻醉开始到手术结束。经过预处理后，生成了一个包含3000张TEE图像及其相应标签的数据集。该数据集在患者层面上以8:1:1的比例随机分为训练集、验证集和测试集。训练集和验证集用于训练用于左心室分割的UNeXt、U-Net、TransUNet和注意力U-Net模型。使用骰子相似系数（DSC）和交并比（IoU）来评估每个模型的分割性能，并采用Kruskal-Wallis检验来分析DSC差异的显著性。在测试集上，UNeXt模型的平均DSC达到88.60%，优于U-Net（87.76%）、TransUNet（85.75%，<0.05）和注意力U-Net（79.98%；<0.05）。此外，与U-Net（185.12 ms）、TransUNet（209.08 ms）和注意力U-Net（201.13 ms）相比，UNeXt模型的参数数量更少（147万），浮点运算次数更少（22.8亿次），每张图像的平均推理时间更短（141.73 ms）。UNeXt的平均IoU（77.60%）也高于U-Net（76.61%）、TransUNet（77.35%）和注意力U-Net（68.86%）。本研究率先构建了大规模的TEE A4CV数据集，并将UNeXt应用于TEE A4CV中的左心室分割。所提出的方法可用于TEE A4CV中左心室的自动分割。
