LiverUSRecon: Automatic 3D Reconstruction and Volumetry of the Liver with a Few Partial Ultrasound Scans
<|reference_start|>LiverUSRecon: Automatic 3D Reconstruction and Volumetry of the Liver with a Few Partial Ultrasound Scans: 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.<|reference_end|>
@article{sivayogaraj2024liverusrecon:,
    title={LiverUSRecon: Automatic 3D Reconstruction and Volumetry of the Liver
  with a Few Partial Ultrasound Scans},
    author={Kaushalya Sivayogaraj, Sahan T. Guruge, Udari Liyanage, Jeevani
  Udupihille, Saroj Jayasinghe, Gerard Fernando, Ranga Rodrigo, M. Rukshani
  Liyanaarachchi},
    journal={arXiv preprint arXiv:2406.19336},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19336},
    primaryClass={eess.IV cs.CV}
}