Automatic Registration Method Research Articles

A novel evaluation model of image registration for cone-beam computed tomography guided lung cancer radiotherapy.

The aim of the study was to establish a weighted comprehensive evaluation model (WCEM) of image registration for cone-beam computed tomography (CBCT) guided lung cancer radiotherapy that considers the geometric accuracy of gross target volume (GTV) and organs at risk (OARs), and assess the registration accuracy of different image registration methods to provide clinical references. The planning CT and CBCT images of 20 lung cancer patients were registered using diverse algorithms (bony and grayscale) and regions of interest (target, ipsilateral, and body). We compared the coverage ratio (CR) of the planning target volume (PTVCT) to GTVCBCT, as well as the dice similarity coefficient (DSC) of the GTV and OARs, considering the treatment position across various registration methods. Furthermore, we developed a mathematical model to assess registration results comprehensively. This model was evaluated and validated using CRFs across four automatic registration methods. The grayscale registration method, coupled with the registration of the ipsilateral structure, exhibited the highest level of automatic registration accuracy, the DSC were 0.87 ± 0.09 (GTV), 0.71 ± 0.09 (esophagus), 0.74 ± 0.09 (spinal cord), and 0.91 ± 0.05 (heart), respectively. Our proposed WCEM proved to be both practical and effective. The results clearly indicated that the grayscale registration method, when applied to the ipsilateral structure, achieved the highest CRF score. The average CRF scores, excellent rates, good rate and qualification rates were 58 ± 26, 40%, 75%, and 85%, respectively. This study successfully developed a clinically relevant weighted evaluation model for CBCT-guided lung cancer radiotherapy. Validation confirmed the grayscale method's optimal performance in ipsilateral structure registration.

RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge

Background and objectiveThe automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated costs. Nevertheless, the slide preparation is different for each stain and the tissue undergoes complex and large deformations. Therefore, a robust, efficient, and accurate registration method is highly desired by the scientific community and hospitals specializing in digital pathology. MethodsWe propose a two-step hybrid method consisting of (i) deep learning- and feature-based initial alignment algorithm, and (ii) intensity-based nonrigid registration using the instance optimization. The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain. The registration time is low, allowing one to perform efficient registration even for large datasets. The method was proposed for the ACROBAT 2023 challenge organized during the MICCAI 2023 conference and scored 1st place. The method is released as open-source software. ResultsThe proposed method is evaluated using three open datasets: (i) Automatic Nonrigid Histological Image Registration Dataset (ANHIR), (ii) Automatic Registration of Breast Cancer Tissue Dataset (ACROBAT), and (iii) Hybrid Restained and Consecutive Histological Serial Sections Dataset (HyReCo). The target registration error (TRE) is used as the evaluation metric. We compare the proposed algorithm to other state-of-the-art solutions, showing considerable improvement. Additionally, we perform several ablation studies concerning the resolution used for registration and the initial alignment robustness and stability. The method achieves the most accurate results for the ACROBAT dataset, the cell-level registration accuracy for the restained slides from the HyReCo dataset, and is among the best methods evaluated on the ANHIR dataset. ConclusionsThe article presents an automatic and robust registration method that outperforms other state-of-the-art solutions. The method does not require any fine-tuning to a particular dataset and can be used out-of-the-box for numerous types of microscopic images. The method is incorporated into the DeeperHistReg framework, allowing others to directly use it to register, transform, and save the WSIs at any desired pyramid level (resolution up to 220k x 220k). We provide free access to the software. The results are fully and easily reproducible. The proposed method is a significant contribution to improving the WSI registration quality, thus advancing the field of digital pathology.

Evaluation of a novel patient-specific quality assurance phantom for robotic single-isocentre, multiple-target stereotactic radiosurgery, and stereotactic radiotherapy.

To evaluate patient-specific quality assurance (PSQA) of 3 targets in a single delivery using a novel film-based phantom. The phantom was designed to rotate freely as a sphere and could measure 3 targets with film in a single delivery. After identifying the coordinates of 3 targets in the skull, the rotation angles about the equator and meridian were computed for optimal phantom setup, ensuring the film plane intersected the 3 targets. The plans were delivered on the CyberKnife system using fiducial tracking. The irradiated films were scanned and processed. All films were analysed using 3 gamma criteria. Fifteen CyberKnife test plans with 3 different modalities were delivered on the phantom. Both automatic and marker-based registration methods were applied when registering the irradiated film and dose plane. Gamma analysis was performed using a 3%/1 mm, 2%/1 mm, and 1%/1 mm criteria with a 10% threshold. For the automatic registration method, the passing rates were 98.2% ± 1.9%, 94.2% ± 3.7%, and 80.9% ± 6.3%, respectively. For the marker-based registration approach, the passing rates were 96.4% ± 2.7%, 91.7% ± 4.3%, and 78.4% ± 6.2%, respectively. A novel spherical phantom was evaluated for the CyberKnife system and achieved acceptable PSQA passing rates using TG218 recommendations. The phantom can measure true-composite dose and offers high-resolution results for PSQA, making it a valuable device for robotic radiosurgery. This is the first study on PSQA of 3 targets concurrently on the CyberKnife system.

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

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. 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. The proposed registration method yielded a precision compatible with ear surgery with a 2D mean overlay error of mm for the incus and mm for the round window. The average Hausdorff distance for these 2 targets was mm and 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 18s. 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.

STUDY ON AUTOMATIC REGISTRATION METHOD OF SOURCE DATA FOR CIM BUILDING MODEL CONSTRUCTION

Abstract. The City Information Model (CIM) is a digital model designed to describe, analyze, and manage urban spatial information. It integrates various urban elements, providing decision support for urban planning, design, and management. This study focuses on the construction of building models in CIM, particularly the issue of automatic registration of source data. We propose an automatic registration method based on DWG-format architectural design drawings and SHAPE-format remote sensing building outline information. This method extracts the axis line information of the building from the plan design drawings, indirectly obtains feature points, and selects registration base points, thereby achieving the registration of real estate subdivision data. Meanwhile, by automatically fitting the outer bounding rectangle of the building and dynamically adjusting the angle, this study realizes the automatic registration of the aligned multilayer real estate subdivision data with the building outline data obtained from remote sensing images. This research provides an effective method for the automatic and batch accurate alignment of data for the construction of CIM building models, thereby enhancing the precision and reliability of the modeling.

Computer-assisted preoperative planning system with an automated registration method in prosthesis-driven oral implantology.

Dentition defect including edentulism is a problem that deserves attention, which requires precise preoperative planning. The trajectories of the implants can be determined using a pre-made radiographic template, which is adopted for prosthesis-driven oral implantology. However, existing solutions for the registration between the radiographic template and the patient's CBCT still require manual operation and cause inadequate accuracy. In this study, a pre-operative planning system for prosthesis-driven oral implantology is developed with a novel automated registration method. Based on threshold segmentation and morphological feature filtering, the potential feature points on two sets of CBCTs are, respectively, recognized. The distance features of the point sets are used to predict the optimal solution for point pair matching, after which the automated registration is implemented. The prosthesis-driven planning can be completed according to the results of registration and multi-planar reconstruction. Then, the surgical templates can be designed and fabricated using 3D printing technology based on the planning results and finally used for intra-operative guidance during implant placement. Verification of the proposed method was conducted on three clinical cases. The mean Fiducial Registration Error of 0.13 ± 0.01mm was achieved with great efficiency. The average time was 0.15s for the automatic registration algorithm, and 15.64s for the whole procedure. The proposed method proved to be accurate and robust. The results indicate that it can achieve higher efficiency while maintaining a low error level, which will have great potential clinical applications in the future.

Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery

Established surgical navigation systems for pedicle screw placement have been proven to be accurate, but still reveal limitations in registration or surgical guidance. Registration of preoperative data to the intraoperative anatomy remains a time-consuming, error-prone task that includes exposure to harmful radiation. Surgical guidance through conventional displays has well-known drawbacks, as information cannot be presented in-situ and from the surgeon’s perspective. Consequently, radiation-free and more automatic registration methods with subsequent surgeon-centric navigation feedback are desirable. In this work, we present a marker-less approach that automatically solves the registration problem for lumbar spinal fusion surgery in a radiation-free manner. A deep neural network was trained to segment the lumbar spine and simultaneously predict its orientation, yielding an initial pose for preoperative models, which then is refined for each vertebra individually and updated in real-time with GPU acceleration while handling surgeon occlusions. An intuitive surgical guidance is provided thanks to the integration into an augmented reality based navigation system. The registration method was verified on a public dataset with a median of 100% successful registrations, a median target registration error of 2.7 mm, a median screw trajectory error of 1.6°and a median screw entry point error of 2.3 mm. Additionally, the whole pipeline was validated in an ex-vivo surgery, yielding a 100% screw accuracy and a median target registration error of 1.0 mm. Our results meet clinical demands and emphasize the potential of RGB-D data for fully automatic registration approaches in combination with augmented reality guidance.

Bone marrow sparing oriented multi-model image registration in cervical cancer radiotherapy

Cervical cancer poses a serious threat to the health of women and radiotherapy is one of the primary treatment methods for this condition. However, this treatment is associated with a high risk of causing acute hematologic toxicity. Delineating the bone marrow (BM) for sparing based on computer tomography (CT) images before radiotherapy can effectively avoid this risk. Unfortunately, compared to magnetic resonance (MR) images, CT images lack the ability to express the activity of BM. Therefore, medical practitioners currently manually delineate the BM on CT images by corresponding to MR images. However, the manual delineation of BM is time-consuming and cannot guarantee accuracy due to the inconsistency of the CT-MR multimodal images. This study proposes a multimodal image-oriented automatic registration method for pelvic BM sparing. The proposed method includes three-dimensional (3D) bone point clouds reconstruction and an iterative closest point registration based on a local spherical system for marking BM on CT images. By introducing a joint coordinate system that combines the global Cartesian coordinate system with the local point clouds’ spherical coordinate system, the increasement of point descriptive dimension avoids the local optimal registration and improves the registration accuracy. Experiments on the dataset of patients demonstrate that our proposed method can enhance the multimodal image registration accuracy and efficiency for medical practitioners in BM-sparing of cervical cancer radiotherapy. The method proposed in this contribution might also provide a solution to multimodal registration, especially in multimodal sequential images in other clinical applications, such as the diagnosis of cervical cancer and the preservation of normal organs during radiotherapy.

An automated multi-constraint joint registration method for mobile LiDAR point cloud in repeated areas

Urban digitalization, autonomous driving, and 3D map construction all benefit greatly from the ability of mobile LiDAR systems (MLS) to swiftly capture centimeter-level accuracy point cloud data of urban scenes. However, the positional accuracy of MLS point clouds is affected by factors such as GNSS signal loss and IMU inertial drift, which can lead to ghosting and layering of point clouds in repeated areas, reducing its usability. Point cloud registration which was developed to solve the MLS localization problem has been studied for a long time. Most current registration methods only consider the point clouds globally, so the registration of repeated areas has not received adequate attention; and the few methods that target repeated areas fail to balance registration accuracy and automation. To address the above-mentioned issues, we propose an automatic multi-constraint joint registration method for MLS point clouds in repeated areas. Firstly, based on the strict correspondence between trajectory and point clouds, this method identifies point cloud repeated areas by searching for repeated areas in the trajectory. Secondly, it adaptively segments the point clouds of repeated areas based on changes in trajectory angle and speed. Then, it uses a deep learning network to automatically extract the scene features of these segmented point clouds, which are essentially constrained by a variety of basic features such as points, lines, and surfaces. Finally, these multi-constrained scene features are used to perform joint registration of point clouds in repeated areas. We carried out repeated areas registration experiments in a campus scene using our self-developed MLS. The results show that the best registration combination can produce errors of 0.021 m and 0.033 m, respectively, compared with pre-registration errors of 0.094 m and 0.103 m. Our method specifically considers MLS point clouds in repeated areas and ensures satisfactory accuracy while achieving automatic point cloud registration. It is also expected that the proposed method can be extended to other LiDAR platforms that also have trajectories, such as Airborne Laser Scanning (ALS) and Simultaneous Localization and Mapping (SLAM), to realize high-precision automatic registration of point clouds in repeated areas, with little adaptation to the complex scenes.

Automatic registration method for medium-resolution remote sensing images of coral reefs with morphological information pairing and constrained iterative fining

Automatic registration method for medium-resolution remote sensing images of coral reefs with morphological information pairing and constrained iterative fining

Precise plane registration with multiple geometric feature matching and weighted least squares estimation

Point cloud registration is a fundamental problem for 3D laser scanning technology, which is extensively applied in geographic entity modelling such as 3D reconstruction of urban roads and buildings. Registration accuracy is one of the main focuses for these applications. However, noisy points, limited overlap, varying data sources, and differing measuring accuracy may cause changes between point cloud sets and reduce registration accuracy. This paper introduces an automatic plane registration method for urban roads and building scenes, which does not need manual on-site deployment. Firstly, plane primitives are extracted using voxel-based filtering region growth. Next, corresponding planes for the extracted primitives are identified by leveraging saliency features and constructing adjacency matrices. Finally, plane registration is achieved using a weighted plane coordinate conversion model. Through real-world scene experimentation, an overall accuracy of 10 cm and a segmental registration accuracy of 5–6 cm is achieved with our method, outperforming both feature point-based and global point cloud registration approaches in terms of efficiency and accuracy.

A Novel Approach to Match Individual Trees between Aerial Photographs and Airborne LiDAR Data

Integrating multimodal remote sensing data can optimize the mapping accuracy of individual trees. Yet, one issue that is not trivial but generally overlooked in previous studies is the spatial mismatch of individual trees between remote sensing datasets, especially in different imaging modalities. These offset errors between the same tree on different data that have been geometrically corrected can lead to substantial inaccuracies in applications. In this study, we propose a novel approach to match individual trees between aerial photographs and airborne LiDAR data. To achieve this, we first leveraged the maximum overlap of the tree crowns in a local area to determine the correct and the optimal offset vector, and then used the offset vector to rectify the mismatch on individual tree positions. Finally, we compared our proposed approach with a commonly used automatic image registration method. We used pairing rate (the percentage of correctly paired trees) and matching accuracy (the degree of overlap between the correctly paired trees) to measure the effectiveness of results. We evaluated the performance of our approach across six typical landscapes, including broadleaved forest, coniferous forest, mixed forest, roadside trees, garden trees, and parkland trees. Compared to the conventional method, the average pairing rate of individual trees for all six landscapes increased from 91.13% to 100.00% (p = 0.045, t-test), and the average matching accuracy increased from 0.692 ± 0.175 (standard deviation) to 0.861 ± 0.152 (p = 0.017, t-test). Our study demonstrates that the proposed tree-oriented matching approach significantly improves the registration accuracy of individual trees between aerial photographs and airborne LiDAR data.

Feature-Based Electromagnetic Tracking Registration Using Bioelectric Sensing

Catheter tracking is essential during minimally invasive endovascular procedures, and Electromagnetic (EM) tracking is a widely used technology for this purpose. When preoperative patient images are available, they can be used to guide EM-tracked interventions. However, a registration step between preoperative images and intraoperative EM tracking space is usually required. Most existing solutions for this registration process require manual interactions, which can add additional steps to the workflow. In this paper, a novel automatic feature-based registration method is proposed, based on electric sensing of vascular geometry by the catheter, also known as Bioelectric sensing. The technique employs the Bioelectric sensing capabilities of the catheter to identify vascular features, such as bifurcations, aneurysms, or stenosis. The known EM position of these features is then utilized to register the EM tracking space and the preoperative images. The registration is refined using iterative closest point (ICP) registration algorithms. Unlike existing solutions, the proposed method does not require external markers, interventional imaging, or additional surgeon actions, and hence does not impact the interventional workflow.

Robust H-K Curvature Map Matching for Patient-to-CT Registration in Neurosurgical Navigation Systems

Image-to-patient registration is a coordinate system matching process between real patients and medical images to actively utilize medical images such as computed tomography (CT) during surgery. This paper mainly deals with a markerless method utilizing scan data of patients and 3D data from CT images. The 3D surface data of the patient are registered to CT data using computer-based optimization methods such as iterative closest point (ICP) algorithms. However, if a proper initial location is not set up, the conventional ICP algorithm has the disadvantages that it takes a long converging time and also suffers from the local minimum problem during the process. We propose an automatic and robust 3D data registration method that can accurately find a proper initial location for the ICP algorithm using curvature matching. The proposed method finds and extracts the matching area for 3D registration by converting 3D CT data and 3D scan data to 2D curvature images and by performing curvature matching between them. Curvature features have characteristics that are robust to translation, rotation, and even some deformation. The proposed image-to-patient registration is implemented with the precise 3D registration of the extracted partial 3D CT data and the patient’s scan data using the ICP algorithm.

Validation of a surgical navigation system for hypertensive intracerebral hemorrhage based on mixed reality using an automatic registration method

Validation of a surgical navigation system for hypertensive intracerebral hemorrhage based on mixed reality using an automatic registration method

A robust and automatic CT-3D ultrasound registration method based on segmentation, context, and edge hybrid metric.

The fusion of computed tomography (CT) and ultrasound (US) image can enhance lesion detection ability and improve the success rate of liver interventional radiology. The image-based fusion methods encounter the challenge of registration initialization due to the random scanning pose and limited field of view of US. Existing automatic methods those used vessel geometric information and intensity-based metric are sensitive to parameters and have low success rate. The learning-based methods require a large number of registered datasets for training. The aim of this study is to provide a fully automatic and robust US-3D CT registration method without registered training data and user-specified parameters assisted by the revolutionary deep learning-based segmentation, which can further be used for preparing training samples for the study of learning-basedmethods. We propose a fully automatic CT-3D US registration method by two improved registration metrics. We propose to use 3D U-Net-based multi-organ segmentation of US and CT to assist the conventional registration. The rigid transform is searched in the space of any paired vessel bifurcation planes where the best transform is decided by a segmentation overlap metric, which is more related to the segmentation precision than Dice coefficient. In nonrigid registration phase, we propose a hybrid context and edge based image similarity metric with a simple mask that can remove most noisy US voxels to guide the B-spline transform registration. We evaluate our method on 42 paired CT-3D US datasets scanned with two different US devices from two hospitals. We compared our methods with other exsiting methods with both quantitative measures of target registration error (TRE) and the Jacobian determinent with paired t-test and qualitative registration imagingresults. The results show that our method achieves fully automatic rigid registration TRE of 4.895 mm, deformable registration TRE of 2.995 mm in average, which outperforms state-of-the-art automatic linear methods and nonlinear registration metrics with paired t-test's p value less than 0.05. The proposed overlap metric achieves better results than self similarity description (SSD), edge matching (EM), and block matching (BM) with p values of 1.624E-10, 4.235E-9, and 0.002, respectively. The proposed hybrid edge and context-based metric outperforms context-only, edge-only, and intensity statistics-only-based metrics with p values of 0.023, 3.81E-5, and 1.38E-15, respectively. The 3D US segmentation has achieved mean Dice similarity coefficient (DSC) of 0.799, 0.724, 0.788, and precision of 0.871, 0.769, 0.862 for gallbladder, vessel, and branch vessel,respectively. The deep learning-based US segmentation can achieve satisfied result to assist robust conventional rigid registration. The Dice similarity coefficient-based metrics, hybrid context, and edge image similarity metric contribute to robust and accurateregistration.

Automatic registration of dental CT and 3D scanned model using deep split jaw and surface curvature

Background and objectives: In the medical field, various image registration applications have been studied. In dentistry, the registration of computed tomography (CT) volume data and 3D optically scanned models is essential for various clinical applications, including orthognathic surgery, implant surgical planning, and augmented reality. Our purpose was to present a fully automatic registration method of dental CT data and 3D scanned models.Methods: We use a 2D convolutional neural network to regress a curve splitting the maxilla (i.e., upper jaw) and mandible (i.e., lower jaw) and the points specifying the front and back ends of the crown from the CT data. Using this regressed information, we extract the point cloud and vertices corresponding to the tooth crown from the CT and scanned data, respectively. We introduce a novel metric, called curvature variance of neighbor (CVN), to discriminate between highly fluctuating and smoothly varying regions of the tooth crown. The registration based on CVN enables more accurate fine registration while reducing the effects of metal artifacts. Moreover, the proposed method does not require any preprocessing such as extracting the iso-surface for the tooth crown from the CT data, thereby significantly reducing the computation time.Results: We evaluated the proposed method with the comparison to several promising registration techniques. Our experimental results using three datasets demonstrated that the proposed method exhibited higher registration accuracy (i.e., 2.85, 1.92, and 7.73 times smaller distance errors for individual datasets) and smaller computation time (i.e., 4.12 times faster registration) than one of the state-of-the-art methods. Moreover, the proposed method worked considerably well for partially scanned data, whereas other methods suffered from the unbalancing of information between the CT and scanned data.Conclusions: The proposed method was able to perform fully automatic and highly accurate registration of dental CT data and 3D scanned models, even with severe metal artifacts. In addition, it could achieve fast registration because it did not require any preprocessing for iso-surface reconstruction from the CT data.

Uncertainty assessment of a permanent long-range terrestrial laser scanning system for the quantification of snow dynamics on Hintereisferner (Austria)

A permanently installed terrestrial laser scanner (TLS) helps to investigate surface changes at high spatio-temporal resolution. Previous studies show that the annual and seasonal glacier volume, and subsequently the mass balance, can be measured by TLSs. This study systematically identifies and quantifies uncertainties and their sources of the permanent long-range TLS system at Hintereisferner glacier (Ötztal Alps, Austria) in order to assess its potential and limitations for detecting glaciologically relevant small-scale surface elevation changes, such as snowfall and redistribution events. Five uncertainty sources are analyzed: the registration method, the influence of the instrument and hardware limitations of the TLS, the effect of atmospheric conditions on the laser beam, the scanning geometry, and the uncertainty caused by rasterization. The instrument and hardware limitations cause the largest uncertainty to the TLS data, followed by the scanning geometry and influence of varying atmospheric conditions on the laser beam. The magnitude of each uncertainty source depends on the distance (range) between the TLS and the target surface, showing a strong decrease of the obtained spatial resolution and a concurrent increase in uncertainty with increasing distance. An automated registration method results in an uncertainty of ±0.50 m at grids of 100 by 100 m. After post-processing, a 0.1-m vertical accuracy can be obtained allowing the detection of surface changes of respective magnitudes and especially making it possible to quantify snow dynamics at Hintereisferner.

Influence of surgical position and registration methods on clinical accuracy of navigation systems in brain tumor surgery

The aim of this study was to evaluate the influence of skin distortion due to surgical positioning on the clinical accuracy of the navigation system. The distance errors were measured in four fiducial markers (anterior, posterior, right, and left of the head) after the registration of the navigation system. The distance errors were compared between the surface-merge registration (SMR) method using preoperative imaging and the automatic intraoperative registration (AIR) method using intraoperative imaging. The comparison of the distance errors were performed in various surgical positions. The AIR method had the significant accuracy in the lateral markers than the SMR method (lateral position, 3.8 mm vs. 8.95 mm; p < 0.0001; prone position, 4.5 mm vs. 13.9 mm; p = 0.0001; 5.2 mm vs. 11.5 mm; p = 0.0070). The smallest distance errors were obtained close to the surgical field in the AIR method (3.25–3.85 mm) and in the forehead in the SMR method (3.3–8.1 mm). The AIR method was accurate and recommended for all the surgical positions if intraoperative imaging was available. The SMR method was only recommended for the supine position, because skin distortion was frequently observed in the lateral region.

Reliability of automatic finish line detection for tooth preparation in dental computer-aided software.

This study aimed to investigate the accuracy of automatic tooth finish line registration compared to manual registration with regard to various finish line configurations and dental computer-aided design (CAD) software. Finish line registrations were performed on 15 digital tooth models with different finish line configurations (edge roundness radius = 0 mm, 0.2 mm, and 0.4 mm; edge angle = 30°, 60°, 90°, 120°, and 150°) using automatic and manual methods for designing virtual copings (N = 150). The discrepancies between the registered finish line extracted from the copings and the actual finish line segmented from the digitized tooth model were measured. Three-way analysis of variance (ANOVA) and post-hoc analyses with Bonferroni correction (α = 0.05) were used to analyze the results. The finish line configurations, registration methods, and CAD software interacted with the accuracy of the registered finish line (p = 0.001). The automatic finish line registration method exhibited larger error values than the manual method, especially at high finish line edge roundness and obtuse edge angles for both EXOCAD and R2CAD software (p < 0.001). The difference in dental CAD software affected the registration accuracy in the automatic method (p < 0.001), but not in the manual method (p = 0.676). Finish line registration errors may occur when the automatic registration method is applied to the indistinct edge of tooth preparation. The accuracy of the automatic finish line registration could differ according to the CAD software program.