The development of an accurate and robust positioning system for oral and maxillofacial surgery (OMS) is a challenging task, primarily due to the oral space limitations and line-of-sight occlusions. This paper presents a novel lightweight integrated positioning system for OMS, which can provide practical guidance utilizing only a micro camera installed on the end of the surgical instrument. An efficient region-based pose tracking method for texture-less teeth is proposed, which can use search lines around the object contour and simple local region partitioning strategy to improve pose accuracy. Besides, to deal with the possible partial occlusions of target during surgery, an occlusion-aware weight function is presented and utilized seamlessly in the pose optimization pipeline. This function calculates the pixel-wise occlusion probability using object contour and distance constraint, helping to improve the tracking robustness. Pivot calibration evaluation reveals that the tracking accuracy of the proposed camera-based handpiece is higher than the marker-based handpieces. Comparative experiments demonstrate that proposed pose tracking method has higher accuracy than existing state-of-the-art methods and ablation study confirms the effectiveness of the occlusion handling strategy. The overall positioning experiment indicates that the proposed system has satisfactory static poses stability and positioning accuracy. Furthermore, the main advantage of our system is that it is lighter and more integrated than other systems, which can reduce the system complexity, decrease the risk of line-of-sight occlusion, and lower the surgery cost.Note to Practitioners—This paper is motivated by the problem of restricted oral space constraints and partial occlusions during positioning for OMS. Compared with traditional OMS navigation systems, the designed system is more lightweight and more integrated without other external cameras and additional fiducial markers. Our system can provide practical guidance utilizing only a micro camera installed on the end of the surgical instrument. In addition, an efficient region-based pose tracking method for texture-less teeth is proposed to increase pose accuracy. Since the target can partially be occluded during the procedure, we present a novel occlusion-aware strategy to improve the tracking performance of partial occlusions. Our proposed system achieves a decent balance between positioning accuracy and hardware cost, and can easily be integrated into various dental surgical tools, thus it has tremendous potential for commercialization.

This paper presents a two-step method to automatically and accurately segment the dental crown components from CT images. Firstly, a multi-scale attention based U-Net model is proposed for pulp segmentation, which is embedded with global and local attention modules. The constructed attention modules can automatically aggregate pixel-wise contextual information and focus on catching the real dental pulp region. Secondly, two efficient level set models are proposed: one is the shape constraint-based level set model for enamel and dentin segmentation, the other is the region mutual exclusion-based level set model for neighboring teeth segmentation. The proposed shape constraint term can better handle topology changes of teeth and the region mutual exclusion term can more effectively avoid intersecting segmentation. Besides, a starting slice initialization method is introduced to achieve automatic segmentation, and an accurate contour propagation strategy is developed for slice-by-slice segmentation. We set up a series of comparative experiments for evaluation. Experimental results verify that the proposed method obtains promising performance for each crown component segmentation, and outperforms state-of-the-art tooth segmentation methods in terms of accuracy. This suggests that the proposed method can be used to accurately segment the crown components for precise tooth preparation treatment.Note to Practitioners—The motivation of this work is to reduce the burden on dentists during tooth preparation treatment, which requires accurate segmentation of crown components (i.e., enamel, dentin, and pulp) from dental CT images. Existing methods only focused on the segmentation of teeth or alveolar bone. Therefore, we present a novel automatic segmentation model for the dental crown components with high accuracy. A key strength of this study is the combination of a data-driven method (deep learning) and model-driven methods (level-set), which can provide good accuracy under limited training samples. This ability is highly desirable for practitioners by saving labor-intensive, costly labeling efforts. Furthermore, our proposed method will provide tools to help reduce subjectivity and human errors, as well as streamline and expedite the clinical workflow. This will significantly facilitate tooth preparation automation.

This paper presents a novel lightweight navigation system for oral and maxillofacial surgery (OMS). An external curved checkerboard with self-identifying markers is set as the reference object around the surgical scene. A customized oral clip with a micro camera is designed for oral localization by tracking the external checkerboard. Similarly, the dental handpiece is also equipped with a micro camera, which can be localized like the clip. The spatial model of the markers is provided by binocular stereoscopic reconstruction. A front surface mirror is taken up for the registration between the oral cavity and the camera on the clip. The pivot calibration of the dental handpiece is accomplished by our proposed calibration method. We set up an experimental group and a control group for evaluation. The surgical tools of the experimental group were approximately 30% lighter, 35% less bulky, and 90% cheaper than those of the control group. Our system yielded the comprehensive navigation accuracy of 0.92 mm whereas the accuracy of the control group was 0.87 mm. Results revealed that our system can achieve similar accuracy compared with a prevailing system at a lighter weight, a more compact volume, and a lower cost. Note to Practitioners—The motivation of this work is to reduce the burden on patients and surgeons during OMS. Current commercial navigation systems are still limited by the burden of extra cumbersome fiducial markers and high hardware costs. Their high accuracy benefits from the large size of fiducial markers. To take full advantage of the camera’s localization effect, we propose the concept of “marker-camera inverse projection”, i.e., reversing the roles of the camera and the markers. In this way, cameras on surgical tools detect more points with a more uniform distribution. Our proposed system achieves a decent balance between navigation accuracy and hardware cost, which facilitates the development of surgical tools to be lighter and more economical, and involves tremendous potential for commercialization.

High-precision pose estimation based on visual markers has been a thriving research topic in the field of computer vision. However, the suitability of traditional flat markers on curved objects is limited due to the diverse shapes of curved surfaces, which hinders the development of high-precision pose estimation for curved objects. Therefore, this paper proposes a novel visual marker called CylinderTag, which is designed for developable curved surfaces such as cylindrical surfaces. CylinderTag is a cyclic marker that can be firmly attached to objects with a cylindrical shape. Leveraging the manifold assumption, the cross-ratio in projective invariance is utilized for encoding in the direction of zero curvature on the surface. Additionally, to facilitate the usage of CylinderTag, we propose a heuristic search-based marker generator and a high-performance recognizer as well. Moreover, an all-encompassing evaluation of CylinderTag properties is conducted by means of extensive experimentation, covering detection rate, detection speed, dictionary size, localization jitter, and pose estimation accuracy. CylinderTag showcases superior detection performance from varying view angles in comparison to traditional visual markers, accompanied by higher localization accuracy. Furthermore, CylinderTag boasts real-time detection capability and an extensive marker dictionary, offering enhanced versatility and practicality in a wide range of applications. Experimental results demonstrate that the CylinderTag is a highly promising visual marker for use on cylindrical-like surfaces, thus offering important guidance for future research on high-precision visual localization of cylinder-shaped objects.

An n-order marker field is a special binary matrix whose n×n subregions are all distinct from each other in four orientations. It is commonly used to guide the composing process of position-sensing markers, which can be detected and identified in a camera image with very limited scope or severe visibility problems. Despite the advantages, position-sensing markers are rare and overlooked because generating marker fields is difficult. In this article, we broaden the definition of marker field, making it more powerful and flexible. Then, we propose bWFC (binary wave function collapse) and its high-speed version, fast-bWFC, to solve the generation problem. The methods are packaged into an open-sourced toolkit named HydraMarker, with which, users not only can generate marker fields on laptops within a short period of time, but also can highly customize them: preset values; fields and subregions in any shape; multifold local uniqueness. Comparative results indicate that the proposed method has superior efficiency, quality, and capability. It makes marker field generation accessible to common marker designers, opening up more possibilities for fiducial markers.

As a preliminary step to achieve a long-term goal of developing an automatic dental preparation system for clinical operations, we design and build a miniature robotic system which can manipulate a laser beam to move in three dimensional spaces to remove hard tissue from a target tooth. The dental preparation requires the robotic system to own high accuracy, high ablation speed and small size. A 2D galvanometer scanners module is integrated to meet the requirement of a high moving speed of the laser focus. A closed-loop system based on a miniature-sized voice-coil motor and a grating ruler are developed to realize the accurate control of the focus. The overall size of the developed prototype is 108mm×56mm×43mm, which is small enough to be used in close proximity to a patient's mouth. The prototype has been tested by using two different kinds of laser generators, i.e., a nanosecond laser and a picosecond laser. The experiment results show that the robotic system can provide high moving speed of 1000mm/s with good shape accuracy. From the results, we found that nanosecond laser beam can be controlled to ablate zirconia and aluminum, but not suitable to ablate tooth because of tissue carbonization. By selecting suitable parameters of the picosecond laser generator, a target tooth could be ablated to produce a cylinder shape without carbonization. Limitations of the prototype are identified according to the experiment results.