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.

利用部分超声扫描的肝脏分割掩膜与由一组 CT 扫描建立的统计形状模型相结合，通过参数回归网络计算形状参数，实现准确的三维肝脏重建和自动肝容积计算。与放射科医生使用 Childs 方法计算的肝容积相比，我们的方法统计上更接近 CT 扫描估计的结果，并对 US 图像分辨率、用于 SSM 的 CT 扫描数量以及输入 US 扫描数量等进行了验证。据我们所知，这是首个利用少量不完整的 US 扫描和一组 CT 扫描的自动肝容积测量系统。