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Abstract Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.

Abstract In intra-operative navigation, a registration procedure is performed to register the patient’s position to the pre-operative imaging data. The registration process is the main factor that determines accuracy of the navigation feedback. In this study, a novel registration protocol for craniofacial surgery is presented, that utilizes a virtual splint with marker points. The accuracy of the proposed method was evaluated by two observers in five human cadaver heads, for optical and electromagnetic navigation, and compared to maxillary bone-anchored fiducial registration (optical and electromagnetic) and surface-based registration (electromagnetic). The results showed minimal differences in accuracy compared to bone-anchored fiducials at the level of the infra-orbital rim. Both point-based techniques had lower error estimates at the infraorbital rim than surface-based registration, but surface-based registration had the lowest loss of accuracy over target distance. An advantage over existing point-based registration methods (bone-anchored fiducials, existing splint techniques) is that radiological imaging does not need to be repeated, since the need for physical fiducials to be present in the image volume is eradicated. Other advantages include reduction of invasiveness compared to bone-achnored fiducials and a possible reduction of human error in the registration process.

The aims of this study were (1) to quantify the intra-individual variation in the upper airway measurements on supine computed tomography (CT) scans at two different time points; and (2) to identify the most stable parameters of the upper airway measurements over time. Ten subjects with paired CT datasets (3–6 months interval) were studied, using computer software to segment and measure the upper airway. The minimum cross-sectional area of the total airway and all its segments (velopharynx, oropharynx, tongue base, and epiglottis) generally had the largest variation, while the length of the total airway had the lowest variation. Sphericity was the only parameter that was stable over time (relative difference

Orthognathic surgery is a widely performed procedure to correct dentofacial deformities. Virtual treatment planning is an important preparation step. One advantage of the use of virtual treatment planning is the possibility to assess the accuracy of orthognathic surgery. In this study, a tool (OrthoGnathicAnalyser 2.0), which allows for quantification of the accuracy of orthognathic surgery, is presented and validated. In the OrthoGnathicAnalyser 2.0 the accuracy of the osseous chin can now be assessed which was not possible in the earlier version of the OrthoGnathicAnalyser. 30 patients who underwent bimaxillary surgery in combination with a genioplasty were selected from three different centers in the Netherlands. A pre-operative (CB)CT scan, virtual treatment planning and postoperative (CB)CT scan were required for assessing the accuracy of bimaxillary surgery. The preoperative and postoperative (CB)CT scans were aligned using voxel-based matching. Furthermore, voxel-based matching was used to align the pre-operative maxilla, mandible and rami towards their postoperative position whereas surface-based matching was used for aligning the pre-operative chin towards the postoperative position. The alignment resulted in a transformation matrix which contained the achieved translations and rotations. The achieved translations and rotations can be compared to planning values of the virtual treatment plan. To study the reproducibility, two independent observers processed all 30 patients to assess the inter-observer variability. One observer processed the patients twice to assess the intra-observer variability. Both the intra- and inter-observer variability showed high ICC values (> 0.92) and low measurement variations (< 0.673±0.684mm and < 0.654±0.824°). The results of this study show that the OrthoGnathicAnalyser 2.0 has an excellent reproducibility for quantification of skeletal movements between two (CB)CT scans.

The aims of this study were (1) to quantify the intra-individual variation in the upper airway measurements on supine computed tomography (CT) scans at two different time points; and (2) to identify the most stable parameters of the upper airway measurements over time. Ten subjects with paired CT datasets (3-6 months interval) were studied, using computer software to segment and measure the upper airway. The minimum cross-sectional area of the total airway and all its segments (velopharynx, oropharynx, tongue base, and epiglottis) generally had the largest variation, while the length of the total airway had the lowest variation. Sphericity was the only parameter that was stable over time (relative difference

AIM:The purpose of this study was to present and validate an innovative semi-automatic approach to quantify the accuracy of the surgical outcome in relation to 3D virtual orthognathic planning among patients who underwent bimaxillary surgery. MATERIAL AND METHOD:For the validation of this new semi-automatic approach, CBCT scans of ten patients who underwent bimaxillary surgery were acquired pre-operatively. Individualized 3D virtual operation plans were made for all patients prior to surgery. During surgery, the maxillary and mandibular segments were positioned as planned by using 3D milled interocclusal wafers. Consequently, post-operative CBCT scan were acquired. The 3D rendered pre- and postoperative virtual head models were aligned by voxel-based registration upon the anterior cranial base. To calculate the discrepancies between the 3D planning and the actual surgical outcome, the 3D planned maxillary and mandibular segments were segmented and superimposed upon the postoperative maxillary and mandibular segments. The translation matrices obtained from this registration process were translated into translational and rotational discrepancies between the 3D planning and the surgical outcome, by using the newly developed tool, the OrthoGnathicAnalyser. To evaluate the reproducibility of this method, the process was performed by two independent observers multiple times. RESULTS:Low intra-observer and inter-observer variations in measurement error (mean error < 0.25 mm) and high intraclass correlation coefficients (> 0.97) were found, supportive of the observer independent character of the OrthoGnathicAnalyser. The pitch of the maxilla and mandible showed the highest discrepancy between the 3D planning and the postoperative results, 2.72° and 2.75° respectively. CONCLUSION:This novel method provides a reproducible tool for the evaluation of bimaxillary surgery, making it possible to compare larger patient groups in an objective and time-efficient manner in order to optimize the current workflow in orthognathic surgery.

INTRODUCTION:In orbital reconstruction, the optimal location of a predefined implant can be planned preoperatively. Surgical results can be assessed intraoperatively or postoperatively. A novel method for quantifying orbital implant position is introduced. The method measures predictability of implant placement: transformation parameters between planned and resulting implant position are quantified. METHODS:The method was tested on 3 human specimen heads. Computed Tomography scans were acquired at baseline with intact orbits (t0), after creation of the defect (t1) and postoperatively after reconstruction of the defect using a preformed implant (t2). Prior to reconstruction, the optimal implant position was planned on the t0 and t1 scans. Postoperatively, the planned and realized implant position were compared. The t0 and t2 scans were fused using iPlan software and the resulting implant was segmented in the fused t2 scan. An implant reference frame was created (Orbital Implant Positioning Frame); the planned implant was transformed to the reference position using an Iterative Closest Point approach. The segmentation of the resulting implant was also registered on the reference position, yielding rotational (pitch, yaw, roll) as well as translational parameters of implant position. RESULTS:Measurement with the Orbital Implant Positioning Frame proved feasible on all three specimen. The positional outcome provided more thorough and accurate insight in resulting implant position than could be gathered from distance measurements alone. Observer-related errors were abolished from the process, since the method is largely automatic. CONCLUSION:A novel method of quantifying surgical outcome in orbital reconstructive surgery was presented. The presented Orbital Implant Positioning Frame assessed all parameters involved in implant displacement. The method proved to be viable on three human specimen heads. Clinically, the method could provide direct feedback intraoperatively and could improve postoperative evaluation of orbital reconstructive surgery.

ObjectiveTo present and validate a semi-automatic segmentation protocol to enable an accurate 3D reconstruction of the mandibular condyles using cone beam computed tomography (CBCT).Materials and methodsApproval from the regional medical ethics review board was obtained for this study. Bilateral mandibular condyles in ten CBCT datasets of patients were segmented using the currently proposed semi-automatic segmentation protocol. This segmentation protocol combined 3D region-growing and local thresholding algorithms. The segmentation of a total of twenty condyles was performed by two observers. The Dice-coefficient and distance map calculations were used to evaluate the accuracy and reproducibility of the segmented and 3D rendered condyles.ResultsThe mean inter-observer Dice-coefficient was 0.98 (range [0.95-0.99]). An average 90th percentile distance of 0.32 mm was found, indicating an excellent inter-observer similarity of the segmented and 3D rendered condyles. No systematic errors were observed in the currently proposed segmentation protocol.ConclusionThe novel semi-automated segmentation protocol is an accurate and reproducible tool to segment and render condyles in 3D. The implementation of this protocol in the clinical practice allows the CBCT to be used as an imaging modality for the quantitative analysis of condylar morphology.