Wang Dongfang, Zhou Tao, Gao Shangbing, Yang Jian
IEEE Trans Biomed Eng. 2025 Aug 12;PP. doi: 10.1109/TBME.2025.3594704.
The segmentation of ultrasound video objects aims to delineate specific anatomical structures or areas of injury in sequential ultrasound imaging data. Current methods exhibit promising results, but struggle with key aspects of ultrasound video analysis. They insufficiently capture inter-frame object motion, resulting in unsatisfactory segmentation for dynamic or low-contrast scenarios. With the release of SAM2, video object segmentation has advanced significantly. However, its performance in ultrasound videos remains suboptimal due to its design bias toward natural videos and lack of consideration for ultrasound-specific characteristics. We propose a novel EchoSAM2 method to achieve more accurate object segmentation in ultrasound videos.
We propose Echo Flow, which captures motion trends between frames to enhance the modeling of temporal relationships. It also helps suppress interference from non-object regions by leveraging object motion patterns. Furthermore, we propose an Echo Modulation Block (EMB) to seamlessly incorporate Echo Flow into the SAM2 framework, improving the quality of feature representation. To further optimize SAM2's performance during fine-tuning, we present a Gaussian Adapter specifically designed for ultrasound image characteristics.
Extensive experiments on three ultrasound video datasets confirm the effectiveness of our method, achieving state-of-the-art results. On the EUDP dataset, our model achieves a Dice of 85.49%, outperforming the second-best method by 3.19%. Models trained on HMC-QU and CAMUS achieve the best generalization when tested on each other's unseen test sets.
The introduction of Echo Flow, along with other supporting modules, enhances both segmentation accuracy and the model's generalizability.
Accurate segmentation of ultrasound video objects enhances diagnostic accuracy and consistency, thereby increasing overall clinical value.
超声视频对象分割旨在在连续的超声成像数据中勾勒出特定的解剖结构或损伤区域。当前的方法取得了有前景的结果，但在超声视频分析的关键方面仍存在困难。它们对帧间对象运动的捕捉不足，导致在动态或低对比度场景下分割效果不理想。随着SAM2的发布，视频对象分割有了显著进展。然而，由于其对自然视频的设计偏向以及未考虑超声特定特征，其在超声视频中的性能仍不理想。我们提出一种新颖的EchoSAM2方法，以在超声视频中实现更准确的对象分割。
我们提出了Echo Flow，它捕捉帧间的运动趋势以增强时间关系的建模。它还通过利用对象运动模式帮助抑制非对象区域的干扰。此外，我们提出了一个回声调制块（EMB），将Echo Flow无缝集成到SAM2框架中，提高特征表示的质量。为了在微调期间进一步优化SAM2的性能，我们提出了一种专门为超声图像特征设计的高斯适配器。
在三个超声视频数据集上进行的大量实验证实了我们方法的有效性，取得了领先的结果。在EUDP数据集上，我们的模型的Dice系数达到85.49%，比第二好的方法高出3.19%。在HMC - QU和CAMUS上训练的模型在对方的未见测试集上测试时实现了最佳的泛化能力。
Echo Flow以及其他支持模块的引入提高了分割精度和模型的泛化能力。
准确分割超声视频对象提高了诊断准确性和一致性，从而增加了整体临床价值。
