PDF(Pubmed)
Abstract：
Segmenting the left ventricle from the transgastric short-axis views (TSVs) on transesophageal echocardiography (TEE) is the cornerstone for cardiovascular assessment during perioperative management. Even for seasoned professionals, the procedure remains time-consuming and experience-dependent. The current study aims to evaluate the feasibility of deep learning for automatic segmentation by assessing the validity of different U-Net algorithms. A large dataset containing 1388 TSV acquisitions was retrospectively collected from 451 patients (32% women, average age 53.42 years) who underwent perioperative TEE between July 2015 and October 2023. With image preprocessing and data augmentation, 3336 images were included in the training set, 138 images in the validation set, and 138 images in the test set. Four deep neural networks (U-Net, Attention U-Net, UNet++, and UNeXt) were employed for left ventricle segmentation and compared in terms of the Jaccard similarity coefficient (JSC) and Dice similarity coefficient (DSC) on the test set, as well as the number of network parameters, training time, and inference time. The Attention U-Net and U-Net++ models performed better in terms of JSC (the highest average JSC: 86.02%) and DSC (the highest average DSC: 92.00%), the UNeXt model had the smallest network parameters (1.47 million), and the U-Net model had the least training time (6428.65 s) and inference time for a single image (101.75 ms). The Attention U-Net model outperformed the other three models in challenging cases, including the impaired boundary of left ventricle and the artifact of the papillary muscle. This pioneering exploration demonstrated the feasibility of deep learning for the segmentation of the left ventricle from TSV on TEE, which will facilitate an accelerated and objective alternative of cardiovascular assessment for perioperative management.
摘要：
在经食管超声心动图（TEE）上从经胃短轴视图（TSV）分割左心室是围手术期心血管评估的基础。即使是经验丰富的专业人士，该过程仍然耗时且依赖于经验。当前的研究旨在通过评估不同U-Net算法的有效性来评估深度学习用于自动分割的可行性。回顾性收集了一个包含1388例TSV采集的大型数据集，该数据集来自451例患者（32％的女性，平均年龄53.42岁），在2015年7月至2023年10月期间接受围手术期TEE。通过图像预处理和数据增强，训练集中包含3336张图像，验证集中的138个图像，和测试集中的138个图像。四个深度神经网络(U-Net，注意U-Net，UNet++,和UNeXt）用于左心室分割，并根据测试集上的Jaccard相似系数（JSC）和Dice相似系数（DSC）进行比较，以及网络参数的数量，培训时间,和推理时间。注意U-Net和U-Net++模型在JSC（最高平均JSC：86.02%）和DSC（最高平均DSC：92.00%）方面表现更好，UNeXt模型的网络参数最小(147万)，U-Net模型的训练时间（6428.65s）和推断时间（101.75ms）最少。注意力U-Net模型在挑战性案例中优于其他三个模型，包括左心室边界受损和乳头状肌伪影。这一开创性的探索证明了深度学习在TEE上从TSV分割左心室的可行性，这将促进心血管评估的加速和客观替代围手术期管理。
