OBJECTIVE: Mandibular reconstruction using fibula free flap is a challenging surgical procedure. To assist osteotomies, computer-assisted surgery (CAS) can be used. Nevertheless, precise registration is required and often necessitates anchored markers that disturb the patient and clinical flow. This work proposes a new contactless surface-based method adapted to featureless anatomies such as fibula to achieve a fast, precise, and reproducible registration.
METHODS: Preoperatively, a CT-scan of the patient is realized and osteotomies are virtually planned. During surgery, a structured light camera digitizes the fibula. The obtained intraoperative point cloud is coarsely registered with the preoperative model using 3 points defined in the CT-scan and located on the patient\'s bone with a laser beam. Then, a fine registration is performed using an ICP algorithm. The registration accuracy was evaluated comparing the position of points engraved in a 3D-printed fibula with their position in the registered model and evaluating resulting osteotomies. Accuracy and execution time were compared to a conventional stylus-based registration method. The work was validated in vivo.
RESULTS: The experiment performed on a 3D-printed model showed that execution time is equivalent to surface-based registration using a stylus, with a better accuracy (mean TRE of 0.9 mm vs 1.3 mm using stylus) and guarantee good osteotomies. The preliminary in vivo study proved the feasibility of the method.
CONCLUSIONS: The proposed contactless surface-based registration method using structured light camera gave promising results in terms of accuracy and execution speed and should be useful to implement CAS for mandibular reconstruction.

A mannequin head was digitized using a reference scanner (Scan in a Box) to acquire the reference mesh. Subsequently it was scanned with a structured light scanner (Einscan Pro HD), a stereophotogrammetry scanner (RayFace100) and a laser scanner (Proface 3D Mid) to acquire test meshes.Resulting meshes were delineated in four horizontal areas and discrepancies calculated for the complete face and different facial partitions. One-way Anova and pairwise comparisons tests were used to compare trueness and precision between scanners across different areas. Significant differences were detected among scanners for complete face (F (3, 27) =776, P ⟨ 0.01)) and for delineated face areas (F (11, 99) =200.1, P ⟨ 0.01)). Einscan had significantly higher accuracy for the complete face (P⟨0.01) and significantly higher trueness for each facial partition compared to other scanners. RayFace had significantly higher trueness when scanning the middle part of face compared to other facial parts. Proface had significantly lower upper facial third trueness compared to other facial parts. All scanners had accuracy levels below the 2.00mm threshold. Facial scanning accuracy was influenced per scanner used. Scanning trueness per device was influenced by location of surface area. All scanners had accuracy levels within the acceptable accuracy threshold.

3D printing is being increasingly adopted in radiation oncology for printing highly conformal medical devices for treatment. Optical surface reconstruction technologies have been shown to be useful for 3D printing applications due to their higher spatial resolution, non-ionising radiation imaging and will likely supplement existing radiographic imaging techniques in the future.

UNASSIGNED: Geometric morphometrics is a powerful approach to capture and quantify morphological shape variation. Both 3D digitizer arms and structured light surface scanners are portable, easy to use, and relatively cheap, which makes these two capturing devices obvious choices for geometric morphometrics. While digitizer arms have been the \"gold standard\", benefits of having full 3D models are manifold. We assessed the measurement error and investigate bias associated with the use of an open-source, high-resolution structured light scanner called SeeMaLab against the popular Microscribe 3D digitizer arm.
UNASSIGNED: The analyses were based on 22 grey seal (Halichoerus grypus) skulls. 31 fixed anatomical landmarks were annotated both directly using a Microscribe 3D digitizer and on reconstructed 3D digital models created from structured light surface scans. Each skull was scanned twice. Two operators annotated the landmarks, each twice on all the skulls and 3D models, allowing for the investigation of multiple sources of measurement error. We performed multiple Procrustes ANOVAs to compare the two devices in terms of within- and between-operator error, to quantify the measurement error induced by device, to compare between-device error with other sources of variation, and to assess the level of scanning-related error. We investigated the presence of general shape bias due to device and operator.
UNASSIGNED: Similar precision was obtained with both devices. If landmarks that were identified as less clearly defined and thus harder to place were omitted, the scanner pipeline would achieve higher precision than the digitizer. Between-operator error was biased and seemed to be smaller when using the scanner pipeline. There were systematic differences between devices, which was mainly driven by landmarks less clearly defined. The factors device, operator and landmark replica were all statistically significant and of similar size, but were minor sources of total shape variation, compared to the biological variation among grey seal skulls. The scanning-related error was small compared to all other error sources.
UNASSIGNED: As the scanner showed precision similar to the digitizer, a scanner should be used if the advantages of obtaining detailed 3D models of a specimen are desired. To obtain high precision, a pre-study should be conducted to identify difficult landmarks. Due to the observed bias, data from different devices and/or operators should not be combined when the expected biological variation is small, without testing the landmarks for repeatability across platforms and operators. For any study necessitating the combination of landmark measurements from different operators, the scanner pipeline will be better suited. The small scanning-related error indicates that by following the same scanning protocol, different operators can be involved in the scanning process without introducing significant error.

OBJECTIVE: Osteophytes are common radiographic markers of osteoarthritis. However, they are not accurately depicted using conventional imaging, thus hampering surgical interventions that rely on pre-operative images. Studies have shown that ultrasound (US) is promising at detecting osteophytes and monitoring the progression of osteoarthritis. Furthermore, three-dimensional (3D) ultrasound reconstructions may offer a means to quantify osteophytes. The purpose of this study was to compare the accuracy of osteophyte depiction in the knee joint between 3D US and conventional computed tomography (CT).
METHODS: Eleven human cadaveric knees were pre-screened for the presence of osteophytes. Three osteoarthritic knees were selected, and then, 3D US and CT images were obtained, segmented, and digitally reconstructed in 3D. After dissection, high-resolution structured light scanner (SLS) images of the joint surfaces were obtained. Surface matching and root mean square (RMS) error analyses of surface distances were performed to assess the accuracy of each modality in capturing osteophytes. The RMS errors were compared between 3D US, CT and SLS models.
RESULTS: Average RMS error comparisons for 3D US versus SLS and CT versus SLS models were 0.87 mm ± 0.33 mm (average ± standard deviation) and 0.95 mm ± 0.32 mm, respectively. No statistical difference was found between 3D US and CT. Comparative observations of imaging modalities suggested that 3D US better depicted osteophytes with cartilage and fibrocartilage tissue characteristics compared to CT.
CONCLUSIONS: Using 3D US can improve the depiction of osteophytes with a cartilaginous portion compared to CT. It can also provide useful information about the presence and extent of osteophytes. Whilst algorithm improvements for automatic segmentation and registration of US are needed to provide a more robust investigation of osteophyte depiction accuracy, this investigation puts forward the potential application for 3D US in routine diagnostic evaluations and pre-operative planning of osteoarthritis.

Foot impression evidence recovered from crime scenes can be available in the form of barefoot prints, sock-clad footprints, or as impressions within footwear. In some cases, suspects leave their footwear at the scene of the crime, and the insoles from the footwear can be important in linking a person to the footwear. The application of 3D data-collecting technology is becoming more and more popular within forensic science and has been used to recover footwear impression evidence. The present study is a feasibility study to discover if 3D data capturing devices can be applied to insoles; to capture the footprint impression for measurement using the Gunn method (a method used in forensic podiatry casework). Three different methods of data capture were conducted; Adobe Photoshop, MeshLab, and calipers used directly on the insole. Paired t-tests and Intraclass Correlation Coefficient (ICC) were conducted for all three data capture methods. Seven measurements used in this study were significantly different across all three methods. ICC scores were moderate to excellent for the Photoshop method, poor to good for the 3D method, and moderate to excellent for the Direct method.

During craniotomies, a portion of the calvarium or skull is removed to gain access to the intracranial space. When it is not possible to re-implant the flap, surgeons may repair the defect intraoperatively or at a later date. With larger defects being more difficult to repair intraoperatively, we investigated a method for the creation of patient-specific moulds for ad hoc bone flap reconstruction using rapid prototyping. Patient-specific moulds were created based on light scanned models of the defect, using custom software and rapid prototyping. Polymethylmethacrylate bone implants were created for three retrospective craniotomy cases and evaluated based on original flap and skull reconstruction accuracy. Bone implants created using our moulding method reconstruct the original flap and skull with an average reconstruction accuracy of 0.82 and 1.3 mm, respectively. Average skull reconstruction accuracy obtained by surgeons performing freehand implant reconstruction was 1.49 mm. Time needed to generate moulds was between 2 h and 45 min and 6 h and 20 min. Improvements to current printing technology will make this procedure technically feasible for future cranial procedures.

OBJECTIVE: Structured light scanning is a promising inexpensive and accurate intraoperative imaging modality. Integration of these scanners in surgical workflows has the potential to enable rapid registration and augment preoperative imaging, in a practical and timely manner in the operating theatre. Previously, we have demonstrated the intraoperative feasibility of such scanners to capture anatomical surface information with high accuracy. The purpose of this study was to investigate the feasibility of automatically characterizing anatomical tissues from textural and spatial information captured by such scanners using machine learning. Assisted or automatic identification of relevant components of a captured scan is essential for effective integration of the technology in surgical workflow.
METHODS: During a clinical study, 3D surface scans for seven total knee arthroplasty patients were collected, and textural and spatial features for cartilage, bone, and ligament tissue were collected and annotated. These features were used to train and evaluate machine learning models. As part of our preliminary preparation, three fresh-frozen knee cadaver specimens were also used where 3D surface scans with texture information were collected during different dissection stages. The resulting models were manually segmented to isolate texture information for muscles, tendon, cartilage, and bone. This information, and detailed labels from dissections, provided an in-depth, finely annotated dataset for building machine learning classifiers.
RESULTS: For characterizing bone, cartilage, and ligament in the intraoperative surface models, random forest and neural network-based models achieved an accuracy of close to 80%, whereas an accuracy of close to 90% was obtained when only characterizing bone and cartilage. Average accuracy of 76-82% was reached for cadaver data in two-, three-, and four-class tissue separation.
CONCLUSIONS: The results of this project demonstrate the feasibility of machine learning methods to accurately classify multiple types of anatomical tissue. The ability to automatically characterize tissues in intraoperatively collected surface models would streamline the surgical workflow of using structured light scanners-paving the way to applications such as 3D documentation of surgery in addition to rapid registration and augmentation of preoperative imaging.

BACKGROUND: To evaluate the three-dimensional (3D) changes after mandibular setback surgery (MSS) in skeletal Class III malocclusion using cone-beam computed tomography (CBCT) and a structured light-based scanner.
METHODS: Twenty-eight adult Korean patients with skeletal Class III malocclusion treated by MSS were evaluated. CBCT and facial scan images were recorded one week before and six months after surgery. To use an identical 3D coordinate system, superimposition was performed, and nine skeletal and 18 soft tissue landmarks were identified. Changes in the landmarks and correlation coefficients and ratios between hard and soft tissue changes were evaluated. Paired t test and Pearson\'s correlation test were performed.
RESULTS: After MSS, the amount of transverse correction was 2.45 mm; mandibular setback, 5.80 mm; and vertical reduction, 1.64 mm at the menton, on average. In the transverse axis, there were significant changes and correlations in the lips and chin and an increasing gradient of ratios from the lower lip to the chin. In the anteroposterior axis, the lower lip and chin moved backward significantly and showed notable correlation with hard tissue movement. In the vertical axis, significant upward movement was observed in the landmarks related to the chin, but only lower facial height was significantly decreased.
CONCLUSIONS: Soft tissue changes according to hard tissue movement after MSS exhibited a distinct pattern of an increasing gradient from the lips to the chin in a transverse aspect.

Access to adequate anatomical specimens can be an important aspect in learning the anatomy of domestic animals. In this study, the authors utilized a structured light scanner and fused deposition modeling (FDM) printer to produce highly accurate animal skeletal models. First, various components of the bovine skeleton, including the femur, the fifth rib, and the sixth cervical (C6) vertebra were used to produce digital models. These were then used to produce 1:1 scale physical models with the FDM printer. The anatomical features of the digital models and three-dimensional (3D) printed models were then compared with those of the original skeletal specimens. The results of this study demonstrated that both digital and physical scale models of animal skeletal components could be rapidly produced using 3D printing technology. In terms of accuracy between models and original specimens, the standard deviations of the femur and the fifth rib measurements were 0.0351 and 0.0572, respectively. All of the features except the nutrient foramina on the original bone specimens could be identified in the digital and 3D printed models. Moreover, the 3D printed models could serve as a viable alternative to original bone specimens when used in anatomy education, as determined from student surveys. This study demonstrated an important example of reproducing bone models to be used in anatomy education and veterinary clinical training. Anat Sci Educ 11: 73-80. © 2017 American Association of Anatomists.