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首页> 外文会议>Chinese conference on pattern recognition and computer vision >Siamese Spatial Pyramid Matching Network with Location Prior for Anatomical Landmark Tracking in 3-Dimension Ultrasound Sequence
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Siamese Spatial Pyramid Matching Network with Location Prior for Anatomical Landmark Tracking in 3-Dimension Ultrasound Sequence

机译：具有位置优先权的连体空间金字塔匹配网络，用于在3维超声序列中进行解剖地标跟踪。

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Accurate motion tracking of the liver target is crucial in image-guided intervention therapy. Compared with other imaging modalities, ultrasound is appealing choice as it provides accurate and real-time anatomical information surrounding lesions. Besides, compared with 2-dimensional ultrasound (2DUS) image, 3-dimensional ultrasound (3DUS) image shows the spatial structure and real lesion motion pattern in patient so that it is an ideal choice for image-guided intervention. In this work, we develop Siamese Spatial Pyramid Matching Network (SSPMNet) to track anatomical landmark in 3DUS sequences. SSPMNet mainly consists of two parts, namely feature extraction network and decision network. Feature extraction network with fully convolu-tional neural (FCN) layers is employed to extract the deep feature in 3DUS image. Spatial Pyramid Pooling (SPP) layer is connected to the end of feature extraction network to generate multiple-level and robust anatomical structure features. In decision network, three fully connected layers are used to compute the similarity between features. Moreover, with the prior knowledge of physical movement, we elaborately design a temporal consistency model to reject outliers in tracking results. Proposed algorithm is evaluated on the Challenge of Liver Ultrasound Tracking (CLUST) across 16 3DUS sequences, yielding 1.89 ± 1.14 mm mean compared with manual annotations. Moreover, extensive ablation study proves that the leading tracking result can benefit from hierarchical feature extraction by SPP. Besides proposed algorithm is not sensitive to sampled sub-volume size. Therefore, proposed algorithm is potential for accurate anatomical landmark tracking in ultrasound-guided intervention.
机译：肝脏目标的精确运动跟踪在图像引导干预治疗中至关重要。与其他成像方式相比，超声是吸引人的选择，因为它可以提供围绕病变的准确且实时的解剖信息。此外，与二维超声（2DUS）图像相比，三维超声（3DUS）图像显示了患者的空间结构和实际病变运动模式，因此它是图像引导干预的理想选择。在这项工作中，我们开发了暹罗空间金字塔匹配网络（SSPMNet）来跟踪3DUS序列中的解剖标志。 SSPMNet主要由特征提取网络和决策网络两部分组成。具有全卷积神经（FCN）层的特征提取网络用于提取3DUS图像中的深层特征。空间金字塔池（SPP）层连接到特征提取网络的末端，以生成多级且健壮的解剖结构特征。在决策网络中，三个完全连接的层用于计算要素之间的相似度。此外，利用对身体运动的先验知识，我们精心设计了一个时间一致性模型，以排除跟踪结果中的异常值。在16个3DUS序列的肝超声追踪挑战（CLUST）上对提出的算法进行了评估，与手动注释相比，该算法产生的平均值为1.89±1.14 mm。此外，广泛的消融研究证明，领先的跟踪结果可从SPP的分层特征提取中受益。此外，提出的算法对采样子体积的大小不敏感。因此，提出的算法在超声引导的介入中对精确的解剖界标追踪具有潜在的可能性。

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