Abstract
Intraoperative fluoroscopy is a frequently used modality in minimally invasive orthopedic surgeries. Aligning the intraoperatively acquired X-ray image with the preoperatively acquired 3D model of a computed tomography (CT) scan reduces the mental burden on surgeons induced by the overlapping anatomical structures in the acquired images. This paper proposes a fully automatic registration method that is robust to extreme viewpoints and does not require manual annotation of landmark points during training. It is based on a fully convolutional neural network (CNN) that regresses the scene coordinates for a given X-ray image. The scene coordinates are defined as the intersection of the back-projected rays from a pixel toward the 3D model. Training data for a patient-specific model were generated through a realistic simulation of a C-arm device using preoperative CT scans. In contrast, intraoperative registration was achieved by solving the perspective-n-point (PnP) problem with a random sample and consensus (RANSAC) algorithm. Experiments were conducted using a pelvic CT dataset that included several real fluoroscopic (X-ray) images with ground truth annotations. The proposed method achieved an average mean target registration error (mTRE) of 3.79+/1.67 mm in the 50th percentile of the simulated test dataset and projected mTRE of 9.65+/−4.07 mm in the 50th percentile of real fluoroscopic images for pelvis registration. The code is available at https://github.com/Pragyanstha/SCR-Registration.
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Acknowledgement
This work was partially supported by a grant from JSPS KAKENHI grant number JP23K08618. This study (in part) used the computational resources for Cygnus provided by the Multidisciplinary Cooperative Research Program at the Center for Computational Sciences, University of Tsukuba, Japan.
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Shrestha, P., Xie, C., Shishido, H., Yoshii, Y., Kitahara, I. (2023). X-Ray to CT Rigid Registration Using Scene Coordinate Regression. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_74
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