Abstract
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.
Abstract (translated)
先前关于心超分割的研究主要集中在胸骨旁长轴视图中的左心室。本研究评估了深度学习模型在胸骨旁短轴心超（PSAX-echo）中对心室分割的效果。通过不同视角的心超图像进行心脏结构的分割，有助于计算重要指标，并可能用于诊断心血管和肺部疾病及其他心肌病。使用小型数据集来评价最先进的模型性能可以揭示它们是否能够在有限的数据下提高表现。 本研究在33名志愿者女性中进行了PSAX-echo检查，一名有经验的心脏病专家从387个扫描图像中确定了舒张末期和收缩末期的帧，并由专业观察员手动描记心脏结构轮廓。预处理后的描记图用于训练两种特定领域（Unet-Resnet101 和 Unet-ResNet50）以及四种通用领域的深度学习模型（三种Segment Anything (SAM) 变体和Detectron2）。使用Dice相似系数(DSC)，Hausdorff距离(HD) 和面积差异(DCSA)来评估这些模型的性能。 Unet-Resnet101 模型在心室分割中表现出优越性，其平均DSC、HD和DCSA分别为0.83、4.93像素和106平方像素。微调后的MedSAM模型分别以0.82的DSC、6.66像素的HD以及1252平方像素的DCSA表现，而Detectron2模型则为0.78的DSC、2.12像素的HD和116平方像素的DCSA。 研究表明深度学习模型适合用于PSAX-echo中左右心室的分割。本研究证明，在处理小规模且本地采集的数据集时，特定领域训练的Unet-ResNet等模型比通用领域的分割模型具有更高的准确度。
URL
https://arxiv.org/abs/2503.08970
