Robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR) is currently
navigated by 2D fluoroscopy which is insufficiently informative. Previously, a
semi-automatic 3D shape instantiation method was developed to instantiate the
3D shape of a main, deployed, and fenestrated stent graft from a single
fluoroscopy projection in real-time, which could help 3D FEVAR navigation and
robotic path planning. This proposed semi-automatic method was based on the
Robust Perspective-5-Point (RP5P) method, graft gap interpolation and
semi-automatic multiple-class marker center determination. In this paper, an
automatic 3D shape instantiation could be achieved by automatic multiple-class
marker segmentation and hence automatic multiple-class marker center
determination. Firstly, the markers were designed into five different shapes.
Then, Equally-weighted Focal U-Net was proposed to segment the fluoroscopy
projections of customized markers into five classes and hence to determine the
marker centers. The proposed Equally-weighted Focal U-Net utilized U-Net as the
network architecture, equally-weighted loss function for initial marker
segmentation, and then equally-weighted focal loss function for improving the
initial marker segmentation. This proposed network outperformed traditional
Weighted U-Net on the class-imbalance segmentation in this paper with reducing
one hyper-parameter - the weight. An overall mean Intersection over Union
(mIoU) of 0.6943 was achieved on 78 testing images, where 81.01% markers were
segmented with a center position error <1.6mm. Comparable accuracy of 3D shape
instantiation was also achieved and stated. The data, trained models and
TensorFlow codes are available on-line.

本文提出了一种基于 Equally-weighted Focal U-Net 的自动化 3D 形状实例化方法，证明该方法在标记分割和 3D 形状实例化方面具有较高的精度，对于机器人路径规划也具有重要意义。

基于平衡聚焦 U-Net 的多类别和类别不平衡标记分割，用于部署的支架内自动化 3D 形态实例化

Towards Automatic 3D Shape Instantiation for Deployed Stent Grafts: 2D  Multiple-class and Class-imbalance Marker Segmentation with Equally-weighted  Focal U-Net