3D reconstruction of the liver for volumetry is important for qualitative analysis and disease diagnosis. Liver volumetry using ultrasound (US) scans, although advantageous due to less acquisition time and safety, is challenging due to the inherent noisiness in US scans, blurry boundaries, and partial liver visibility. We address these challenges by using the segmentation masks of a few incomplete sagittal-plane US scans of the liver in conjunction with a statistical shape model (SSM) built using a set of CT scans of the liver. We compute the shape parameters needed to warp this canonical SSM to fit the US scans through a parametric regression network. The resulting 3D liver reconstruction is accurate and leads to automatic liver volume calculation. We evaluate the accuracy of the estimated liver volumes with respect to CT segmentation volumes using RMSE. Our volume computation is statistically much closer to the volume estimated using CT scans than the volume computed using Childs' method by radiologists: p-value of 0.094 (>0.05) says that there is no significant difference between CT segmentation volumes and ours in contrast to Childs' method. We validate our method using investigations (ablation studies) on the US image resolution, the number of CT scans used for SSM, the number of principal components, and the number of input US scans. To the best of our knowledge, this is the first automatic liver volumetry system using a few incomplete US scans given a set of CT scans of livers for SSM.

翻译：肝脏三维重建对于容积测量至关重要，这有助于定性分析和疾病诊断。尽管超声扫描具有采集时间短和安全性高的优势，但由于超声图像固有的噪声、边界模糊以及肝脏局部可见性等问题，基于超声的肝脏容积测量仍面临挑战。为解决这些问题，我们利用少量不完整的肝脏矢状面超声扫描分割掩膜，并结合基于一组肝脏CT扫描构建的统计形状模型。通过参数回归网络计算形变参数，将标准统计形状模型形变以匹配超声扫描。所得的三维肝脏重建结果精确，可实现自动肝脏容积计算。我们采用均方根误差评估估计肝脏容积相对于CT分割容积的准确性。与放射科医师使用的Childs方法相比，我们的容积计算结果在统计学上更接近CT扫描估计容积：p值为0.094（>0.05）表明CT分割容积与我们的方法无显著差异，而Childs方法则存在显著差异。我们通过超声图像分辨率、用于统计形状模型的CT扫描数量、主成分数量以及输入超声扫描数量等消融实验验证了本方法。据我们所知，这是首个在给定一组用于统计形状模型的肝脏CT扫描前提下，利用少量不完整超声扫描实现全自动肝脏容积测量的系统。