Synthetic white balancing for intra-operative hyperspectral imaging

Hyperspectral imaging shows promise for surgical applications to non-invasively provide spatially-resolved, spectral information. For calibration purposes, a white reference image of a highly-reflective Lambertian surface should be obtained under the same imaging conditions. Standard white references are not sterilizable, and so are unsuitable for surgical environments. We demonstrate the necessity for in situ white references and address this by proposing a novel, sterile, synthetic reference construction algorithm. The use of references obtained at different distances and lighting conditions to the subject were examined. Spectral and color reconstructions were compared with standard measurements qualitatively and quantitatively, using $\Delta E$ and normalised RMSE respectively. The algorithm forms a composite image from a video of a standard sterile ruler, whose imperfect reflectivity is compensated for. The reference is modelled as the product of independent spatial and spectral components, and a scalar factor accounting for gain, exposure, and light intensity. Evaluation of synthetic references against ideal but non-sterile references is performed using the same metrics alongside pixel-by-pixel errors. Finally, intraoperative integration is assessed though cadaveric experiments. Improper white balancing leads to increases in all quantitative and qualitative errors. Synthetic references achieve median pixel-by-pixel errors lower than 6.5% and produce similar reconstructions and errors to an ideal reference. The algorithm integrated well into surgical workflow, achieving median pixel-by-pixel errors of 4.77%, while maintaining good spectral and color reconstruction.

Next-generation Surgical Navigation: Multi-view Marker-less 6DoF Pose Estimation of Surgical Instruments

State-of-the-art research of traditional computer vision is increasingly leveraged in the surgical domain. A particular focus in computer-assisted surgery is to replace marker-based tracking systems for instrument localization with pure image-based 6DoF pose estimation. However, the state of the art has not yet met the accuracy required for surgical navigation. In this context, we propose a high-fidelity marker-less optical tracking system for surgical instrument localization. We developed a multi-view camera setup consisting of static and mobile cameras and collected a large-scale RGB-D video dataset with dedicated synchronization and data fusions methods. Different state-of-the-art pose estimation methods were integrated into a deep learning pipeline and evaluated on multiple camera configurations. Furthermore, the performance impacts of different input modalities and camera positions, as well as training on purely synthetic data, were compared. The best model achieved an average position and orientation error of 1.3 mm and 1.0{\deg} for a surgical drill as well as 3.8 mm and 5.2{\deg} for a screwdriver. These results significantly outperform related methods in the literature and are close to clinical-grade accuracy, demonstrating that marker-less tracking of surgical instruments is becoming a feasible alternative to existing marker-based systems.

Automatic breach detection during spine pedicle drilling based on vibroacoustic sensing

Pedicle drilling is a complex and critical spinal surgery task. Detecting breach or penetration of the surgical tool to the cortical wall during pilot-hole drilling is essential to avoid damage to vital anatomical structures adjacent to the pedicle, such as the spinal cord, blood vessels, and nerves. Currently, the guidance of pedicle drilling is done using image-guided methods that are radiation intensive and limited to the preoperative information. This work proposes a new radiation-free breach detection algorithm leveraging a non-visual sensor setup in combination with deep learning approach. Multiple vibroacoustic sensors, such as a contact microphone, a free-field microphone, a tri-axial accelerometer, a uni-axial accelerometer, and an optical tracking system were integrated into the setup. Data were collected on four cadaveric human spines, ranging from L5 to T10. An experienced spine surgeon drilled the pedicles relying on optical navigation. A new automatic labeling method based on the tracking data was introduced. Labeled data was subsequently fed to the network in mel-spectrograms, classifying the data into breach and non-breach. Different sensor types, sensor positioning, and their combinations were evaluated. The best results in breach recall for individual sensors could be achieved using contact microphones attached to the dorsal skin (85.8\%) and uni-axial accelerometers clamped to the spinous process of the drilled vertebra (81.0\%). The best-performing data fusion model combined the latter two sensors with a breach recall of 98\%. The proposed method shows the great potential of non-visual sensor fusion for avoiding screw misplacement and accidental bone breaches during pedicle drilling and could be extended to further surgical applications.