Real-time 6DoF pose recovery from X-ray images using library-based DRR and hybrid optimization

X-ray coronary centerline extraction based on C-UNet and a multifactor reconnection algorithm

BACKGROUND AND OBJECTIVE

Accurate extraction of the coronary artery centerline is crucial in the processes of coronary artery reconstruction, coronary artery stenosis or lesion detection, and surgical navigation. Furthermore, in clinical medicine, the complex background of angiography, low signal-to-noise ratio, and complex vascular structure make coronary artery centerline extraction challenging. In this study, a direct centerline extraction method is proposed that automatically and accurately extracts vascular centerlines from X-ray coronary angiography images based on deep learning and conventional methods.

METHODS

In this study, a coronary artery centerline extraction method is proposed that comprises two parts: the preliminary centerline extraction network based on U-Net with a residual network, called C-UNet, and the multifactor centerline reconnection algorithm based on the geometric characteristics of blood vessels.

RESULTS

The qualitative and quantitative results demonstrate the effectiveness of the presented method. In this study, three widely used evaluation indices were adopted to evaluate the performance of the method: precision, recall, and F1_Score. The experimental results show that this method can accurately extract coronary artery centerlines.

CONCLUSIONS

The proposed centerline extraction method accurately extracts centerlines from X-ray coronary angiography images and improves both the accuracy and continuity of centerline extraction.

Pose-Dependent Weights and Domain Randomization for Fully Automatic X-Ray to CT Registration

Fully automatic X-ray to CT registration requires a solid initialization to provide an initial alignment within the capture range of existing intensity-based registrations. This work addresses that need by providing a novel automatic initialization, which enables end to end registration. First, a neural network is trained once to detect a set of anatomical landmarks on simulated X-rays. A domain randomization scheme is proposed to enable the network to overcome the challenge of being trained purely on simulated data and run inference on real X-rays. Then, for each patient CT, a fully-automatic patient-specific landmark extraction scheme is used. It is based on backprojecting and clustering the previously trained network's predictions on a set of simulated X-rays. Next, the network is retrained to detect the new landmarks. Finally the combination of network and 3D landmark locations is used to compute the initialization using a perspective-n-point algorithm. During the computation of the pose, a weighting scheme is introduced to incorporate the confidence of the network in detecting the landmarks. The algorithm is evaluated on the pelvis using both real and simulated x-rays. The mean (± standard deviation) target registration error in millimetres is 4.1 ± 4.3 for simulated X-rays with a success rate of 92% and 4.2 ± 3.9 for real X-rays with a success rate of 86.8%, where a success is defined as a translation error of less than 30 mm .