Image-to-Patient Registration in Computer-Assisted Surgery of Head and Neck: State-of-the-Art, Perspectives, and Challenges

Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks

PURPOSE

Patient-to-image registration is a preliminary step required in surgical navigation based on preoperative images. Human intervention and fiducial markers hamper this task as they are time-consuming and introduce potential errors. We aimed to develop a fully automatic 2D registration system for augmented reality in ear surgery.

METHODS

CT-scans and corresponding oto-endoscopic videos were collected from 41 patients (58 ears) undergoing ear examination (vestibular schwannoma before surgery, profound hearing loss requiring cochlear implant, suspicion of perilymphatic fistula, contralateral ears in cases of unilateral chronic otitis media). Two to four images were selected from each case. For the training phase, data from patients (75% of the dataset) and 11 cadaveric specimens were used. Tympanic membranes and malleus handles were contoured on both video images and CT-scans by expert surgeons. The algorithm used a U-Net network for detecting the contours of the tympanic membrane and the malleus on both preoperative CT-scans and endoscopic video frames. Then, contours were processed and registered through an iterative closest point algorithm. Validation was performed on 4 cases and testing on 6 cases. Registration error was measured by overlaying both images and measuring the average and Hausdorff distances.

RESULTS

The proposed registration method yielded a precision compatible with ear surgery with a 2D mean overlay error of 0.65 ± 0.60 mm for the incus and 0.48 ± 0.32 mm for the round window. The average Hausdorff distance for these 2 targets was 0.98 ± 0.60 mm and 0.78 ± 0.34 mm respectively. An outlier case with higher errors (2.3 mm and 1.5 mm average Hausdorff distance for incus and round window respectively) was observed in relation to a high discrepancy between the projection angle of the reconstructed CT-scan and the video image. The maximum duration for the overall process was 18 s.

CONCLUSIONS

A fully automatic 2D registration method based on a convolutional neural network and applied to ear surgery was developed. The method did not rely on any external fiducial markers nor human intervention for landmark recognition. The method was fast and its precision was compatible with ear surgery.

Simulated augmented reality-based calibration of optical see-through head mound display for surgical navigation

PURPOSE

Calibration of an optical see-through head-mounted display is critical for augmented reality-based surgical navigation. While conventional methods have advanced, calibration errors remain significant. Moreover, prior research has focused primarily on calibration accuracy and procedure, neglecting the impact on the overall surgical navigation system. Consequently, these enhancements do not necessarily translate to accurate augmented reality in the optical see-through head mount due to systemic errors, including those in calibration.

METHOD

This study introduces a simulated augmented reality-based calibration to address these issues. By replicating the augmented reality that appeared in the optical see-through head mount, the method achieves calibration that compensates for augmented reality errors, thereby reducing them. The process involves two distinct calibration approaches, followed by adjusting the transformation matrix to minimize displacement in the simulated augmented reality.

RESULTS

The efficacy of this method was assessed through two accuracy evaluations: registration accuracy and augmented reality accuracy. Experimental results showed an average translational error of 2.14 mm and rotational error of 1.06° across axes in both approaches. Additionally, augmented reality accuracy, measured by the overlay regions' ratio, increased to approximately 95%. These findings confirm the enhancement in both calibration and augmented reality accuracy with the proposed method.

CONCLUSION

The study presents a calibration method using simulated augmented reality, which minimizes augmented reality errors. This approach, requiring minimal manual intervention, offers a more robust and precise calibration technique for augmented reality applications in surgical navigation.

A clinical presentation of markerless augmented reality assisted surgery for resection of a dentigerous cyst in the maxillary sinus

Dentigerous cysts (DC) in the maxillary sinus are rare and pose challenges for effective treatment. Despite various available surgical techniques, a definitive approach remains debated. This study introduces a markerless Augmented Reality Assisted Surgery (ARAS) system that utilizes tooth image recognition and surgical simulation to enhance the precision of maxillary sinus DC extractions. Using advanced technology, such as 3-dimensional (3D) intraoral scanning and CT imaging for accurate data capture, the system aligns virtual models with patient anatomy without external markers, demonstrating a minimally invasive surgical solution. The ARAS system enabled precise surgical planning and realization of a DC extraction in the maxillary sinus by creating a bone window in direct contact with the cyst, assisting in complete removal with minimal risk to adjacent structures. The ARAS system may aid surgeons in visualizing patient anatomy during surgery, with overlays of relevant medical images, aiding in precise localization and minimizing tissue damage.