Guan P, Luo H, Guo J, Zhang Y, Jia F. Intraoperative laparoscopic liver surface registration with preoperative CT using mixing features and overlapping region masks. Int J Comput Assist Radiol Surg. 2023 Feb 14. doi: 10.1007/s11548-023-02846-w. Epub ahead
Guan P, Luo H, Guo J, Zhang Y, Jia F. Intraoperative laparoscopic liver surface registration with preoperative CT using mixing features and overlapping region masks. Int J Comput Assist Radiol Surg. 2023 Feb 14. doi: 10.1007/s11548-023-02846-w. Epub ahead
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关 P，罗 H，郭 J，张 Y，贾 F。使用混合特征和重叠区域掩模与术前 CT 进行术中腹腔镜肝脏表面配准。
DOI：
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发表时间：
2023
影响因子：
3
通讯作者：
Fucang Jia
中科院分区：
文献类型：
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作者：
Peidong Guan;Huoling Luo;Jianxi Guo;Yanfang Zhang;Fucang Jia
目的：腹腔镜肝切除术是一种微创手术。增强现实技术可以将术前解剖信息从计算机断层扫描中提取的信息映射到术中立体三维腹腔镜术中重建的肝脏表面。然而，肝脏表面的配准尤其具有挑战性，因为术中表面只是部分可见的，并且受到气腹造成的肝脏大变形的影响。提出了一种基于深度学习的稳健点云配准网络。方法结合点云局部混合特征和全局特征，提出了一种低重叠的肝脏表面配准算法。使用学习的重叠掩模滤除点云的非重叠区域，并使用网络预测重叠区域阈值以调整训练过程。结果在DePoLL(可变形猪腹腔镜肝脏)数据集上验证了算法的有效性。与基线法和其他最新的配准方法相比，我们的方法获得了19.9±2.7 mm的最小目标配准误差。结论本文提出的点云配准方法利用学习的重叠模板滤除点云中的非重叠区域，然后根据混合特征和全局特征对提取的重叠区域点云进行配准，该方法在低重叠肝脏表面配准中是稳健和有效的。
Purpose Laparoscopic liver resection is a minimal invasive surgery. Augmented reality can map preoperative anatomy.information extracted from computed tomography to the intraoperative liver surface reconstructed from stereo 3D laparoscopy..However, liver surface registration is particularly challenging as the intraoperative surface is only partially visible and suffers.from large liver deformations due to pneumoperitoneum. This study proposes a deep learning-based robust point cloud.registration network..Methods This study proposed a low overlap liver surface registration algorithm combining local mixed features and global.features of point clouds. A learned overlap mask is used to filter the non-overlapping region of the point cloud, and a network.is used to predict the overlapping region threshold to regulate the training process..Results We validated the algorithm on the DePoLL (the Deformable Porcine Laparoscopic Liver) dataset. Compared with.the baseline method and other state-of-the-art registration methods, our method achieves minimum target registration error.(TRE) of 19.9 ± 2.7 mm..Conclusion The proposed point cloud registration method uses the learned overlapping mask to filter the non-overlapping.areas in the point cloud, then the extracted overlapping area point cloud is registered according to the mixed features and.global features, and this method is robust and efficient in low-overlap liver surface registration.