PDF(Pubmed)
Abstract：
In this paper, we propose a 3D neurovascular bundles (NVB) segmentation method for ultrasound (US) image by integrating MR and transrectal ultrasound (TRUS) images through MR-TRUS deformable registration. First, 3D NVB was contoured by a physician in MR images, and the 3D MR-defined NVB was then transformed into US images using a MR-TRUS registration method, which models the prostate tissue as an elastic material, and jointly estimates the boundary deformation and the volumetric deformations under the elastic constraint. This technique was validated with a clinical study of 6 patients undergoing radiation therapy (RT) treatment for prostate cancer. The accuracy of our approach was assessed through the locations of landmarks, as well as previous ultrasound Doppler images of patients. MR-TRUS registration was successfully performed for all patients. The mean displacement of the landmarks between the post-registration MR and TRUS images was less than 2 mm, and the average NVB volume Dice Overlap Coefficient was over 89%. This NVB segmentation technique could be a useful tool as we try to spare the NVB in prostate RT, monitor NVB response to RT, and potentially improve post-RT potency outcomes.
摘要：
在本文中,我们提出了一种用于超声（US）图像的3D神经血管束（NVB）分割方法，该方法通过MR-TRUS可变形配准来整合MR和经直肠超声（TRUS）图像。首先,3DNVB由医生在MR图像中勾画轮廓，然后使用MR-TRUS配准方法将3DMR定义的NVB转换为US图像，将前列腺组织建模为弹性材料，并联合估计弹性约束下的边界变形和体积变形。该技术通过对6例接受放射治疗（RT）治疗的前列腺癌患者的临床研究得到了验证。我们的方法的准确性是通过地标的位置来评估的，以及以前患者的超声多普勒图像。所有患者均成功进行了MR-TRUS注册。配准后的MR和TRUS图像之间的标志的平均位移小于2毫米，平均NVB体积骰子重叠系数超过89%。这种NVB分割技术可能是一个有用的工具，因为我们试图在前列腺RT中节省NVB，监控NVB对RT的响应，并可能改善RT后的效力结果。
