Unsupervised Motion Tracking of Left Ventricle in Echocardiography.
Unsupervised Motion Tracking of Left Ventricle in Echocardiography.
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发表时间：
2020-03
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通讯作者：
Duncan JS
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作者：
Ahn SS;Ta K;Lu A;Stendahl JC;Sinusas AJ;Duncan JS
左心室的精确运动跟踪在检测诸如心肌梗塞的损伤后的心脏中的室壁运动异常中是至关重要的。我们提出了一个无监督的运动跟踪框架与生理约束，学习密集的位移场之间的顺序对2-D B型超声心动图图像。目前的深度学习运动跟踪算法需要大量数据来提供地面实况，这对于体内数据集（例如患者数据和动物研究）来说是难以获得的，或者由于固有的超声特性（例如低信噪比和各种图像伪影），在跟踪超声心动图图像之间的运动方面是不成功的。我们设计了一个受U-Net启发的卷积神经网络，它使用手动跟踪的分割作为指导，通过最小化变换后的源帧和原始目标帧之间的差异，在没有地面真实位移场的情况下学习源图像和目标图像之间的位移估计。然后我们惩罚位移场的发散，以加强左心室内的不可压缩性。我们证明了我们的模型在合成和体内犬2-D超声心动图数据集上的性能，通过将其与非刚性配准算法和形状跟踪算法进行比较。我们的研究结果表明，我们的模型对这两种方法的良好性能。
Accurate motion tracking of the left ventricle is critical in detecting wall motion abnormalities in the heart after an injury such as a myocardial infarction. We propose an unsupervised motion tracking framework with physiological constraints to learn dense displacement fields between sequential pairs of 2-D B-mode echocardiography images. Current deep-learning motion-tracking algorithms require large amounts of data to provide ground-truth, which is difficult to obtain for in vivo datasets (such as patient data and animal studies), or are unsuccessful in tracking motion between echocardiographic images due to inherent ultrasound properties (such as low signal-to-noise ratio and various image artifacts). We design a U-Net inspired convolutional neural network that uses manually traced segmentations as a guide to learn displacement estimations between a source and target image without ground-truth displacement fields by minimizing the difference between a transformed source frame and the original target frame. We then penalize divergence in the displacement field in order to enforce incompressibility within the left ventricle. We demonstrate the performance of our model on synthetic and in vivo canine 2-D echocardiography datasets by comparing it against a non-rigid registration algorithm and a shape-tracking algorithm. Our results show favorable performance of our model against both methods.