School of Information Science, Aichi Institute of Technology, Yachikusa, Yakusa-cho, Toyota, 470-0392, Japan.
Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
Int J Comput Assist Radiol Surg. 2017 Jun;12(6):1041-1048. doi: 10.1007/s11548-017-1549-x. Epub 2017 Mar 8.
For safe and reliable laparoscopic surgery, it is important to determine individual differences of blood vessels such as the position, shape, and branching structures. Consequently, a computer-assisted laparoscopy that displays blood vessel structures with anatomical labels would be extremely beneficial. This paper details an automated anatomical labeling method for abdominal arteries and veins extracted from 3D CT volumes.
The proposed method represents a blood vessel tree as a probabilistic graphical model by conditional random fields (CRFs). An adaptive gradient algorithm is adopted for structure learning. The anatomical labeling of blood vessel branches is performed by maximum a posteriori estimation.
We applied the proposed method to 50 cases of arterial and portal phase abdominal X-ray CT volumes. The experimental results showed that the F-measure of the proposed method for abdominal arteries and veins was 94.4 and 86.9%, respectively.
We developed an automated anatomical labeling method to annotate each blood vessel branches of abdominal arteries and veins using CRF. The proposed method outperformed a state-of-the-art method.
为了实现安全可靠的腹腔镜手术，确定血管的个体差异（如位置、形状和分支结构）非常重要。因此，具有解剖标签显示血管结构的计算机辅助腹腔镜将是非常有益的。本文详细介绍了一种从 3D CT 容积中提取腹部动脉和静脉的自动解剖标签方法。
所提出的方法通过条件随机场（CRFs）将血管树表示为概率图形模型。采用自适应梯度算法进行结构学习。通过最大后验估计进行血管分支的解剖标签。
我们将所提出的方法应用于 50 例动脉期和门静脉期腹部 X 射线 CT 容积。实验结果表明，所提出的方法对腹部动脉和静脉的 F 度量分别为 94.4%和 86.9%。
我们开发了一种自动解剖标签方法，使用 CRF 对腹部动脉和静脉的每个血管分支进行注释。所提出的方法优于最先进的方法。
