最先进深度学习模型在胸骨旁短轴超声心动图心室分割中的评估
Evaluation of state-of-the-art deep learning models in the segmentation of the heart ventricles in parasternal short-axis echocardiograms
AI总结：
本研究评估了深度学习模型在胸骨旁短轴超声心动图心室分割中的表现，发现特定领域模型Unet-ResNet101在小数据集上的分割准确性优于通用领域模型。
摘要：
以往关于超声心动图分割的研究主要集中在胸骨旁长轴切面的左心室。本研究评估了深度学习模型在胸骨旁短轴超声心动图（PSAX-echo）心室分割中的表现。对补充超声心动图切面的心室进行分割将允许计算重要指标，有望辅助诊断心肺疾病和其他心肌病。使用小数据集评估最先进模型可以揭示它们是否能在有限数据上提升性能。对33名女性志愿者进行了PSAX-echo检查。经验丰富的心脏病专家从387次扫描中识别出舒张末期和收缩末期帧，专家观察者手动描绘了心脏结构的轮廓。描绘的帧经过预处理并用于创建标签，以训练2个特定领域（Unet-Resnet101和Unet-ResNet50）及4个通用领域（3个Segment Anything（SAM）变体和Detectron2）深度学习模型。使用Dice相似系数（DSC）、Hausdorff距离（HD）和横截面积差异（DCSA）评估模型性能。Unet-Resnet101模型在心室分割中表现最佳，DSC、HD和DCSA平均值分别为0.83、4.93像素和106 pixel2。微调的MedSAM模型性能为0.82、6.66像素和1252 pixel2，而Detectron2模型在相同指标上分别为0.78、2.12像素和116 pixel2。深度学习模型适用于PSAX-echo中左右心室的分割。本研究表明，在使用小型和本地采集数据集时，像Unet-ResNet这样特定领域训练的模型在超声分割上比通用领域分割模型具有更高的准确性。
英文摘要：
Previous studies on echocardiogram segmentation are focused on the left ventricle in parasternal long-axis views. In this study, deep-learning models were evaluated on the segmentation of the ventricles in parasternal short-axis echocardiograms (PSAX-echo). Segmentation of the ventricles in complementary echocardiogram views will allow the computation of important metrics with the potential to aid in diagnosing cardio-pulmonary diseases and other cardiomyopathies. Evaluating state-of-the-art models with small datasets can reveal if they improve performance on limited data. PSAX-echo were performed on 33 volunteer women. An experienced cardiologist identified end-diastole and end-systole frames from 387 scans, and expert observers manually traced the contours of the cardiac structures. Traced frames were pre-processed and used to create labels to train 2 specific-domain (Unet-Resnet101 and Unet-ResNet50), and 4 general-domain (3 Segment Anything (SAM) variants, and the Detectron2) deep-learning models. The performance of the models was evaluated using the Dice similarity coefficient (DSC), Hausdorff distance (HD), and difference in cross-sectional area (DCSA). The Unet-Resnet101 model provided superior performance in the segmentation of the ventricles with 0.83, 4.93 pixels, and 106 pixel2 on average for DSC, HD, and DCSA respectively. A fine-tuned MedSAM model provided a performance of 0.82, 6.66 pixels, and 1252 pixel2, while the Detectron2 model provided 0.78, 2.12 pixels, and 116 pixel2 for the same metrics respectively. Deep-learning models are suitable for the segmentation of the left and right ventricles in PSAX-echo. This study demonstrated that specific-domain trained models such as Unet-ResNet provide higher accuracy for echo segmentation than general-domain segmentation models when working with small and locally acquired datasets.
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