Using structured light three-dimensional surface scanning on living individuals: Key considerations and best practice for forensic medicine

Evaluating the precision and accuracy of digital dental models with a low-cost structured light device

OBJECTIVE

This study evaluated the accuracy and precision of digital models acquisition using a home-built, low-cost scanning system based on the structured light method.

METHODS

a plaster model (PM) was scanned using the experimental device (SL) and a dental desktop scanner (DS). The teeth dimensions of PM and SL models were measured in triplicate, with a caliper and digitally, respectively. The agreement of the measurements of each model was evaluated using the intraclass correlation coefficient, and the validity between the different measurement techniques was assessed using the Bland-Altman analysis. The accuracy and precision of the models were qualitatively investigated using the mesh superposition of the SL and DS models.

RESULTS

A high intraclass correlation coefficient was observed in all models (PM=0.964; SL1=0.998; SL2=0.995; SL3=0.998), and there was no statistical difference between the measurements of the SL models (p>0.05). PM and SL model measurements were found to be in good agreement, with only 3.57% of the observed differences between the same measurement being located outside 95% limits of agreement according to Bland and Altman (0.43 and -0.40 mm). In the superimpositions of SL-SL and SL-DS models, areas of discrepancy greater than 0.5 mm were observed mainly in interproximal, occlusal, and cervical sites.

CONCLUSION

These results indicate that the home-built SL scanning system did not possess sufficient accuracy and precision for many clinical applications. However, the consistency in preserving the dental proportions suggests that the equipment can be used for planning, storage, and simple clinical purposes.

A Review on 3D Scanners Studies for Producing Customized Orthoses

When a limb suffers a fracture, rupture, or dislocation, it is traditionally immobilized with plaster. This may induce discomfort in the patient, as well as excessive itching and sweating, which creates the growth of bacteria, leading to an unhygienic environment and difficulty in keeping the injury clean during treatment. Furthermore, if the plaster remains for a long period, it may cause lesions in the joints and ligaments. To overcome all of these disadvantages, orthoses have emerged as important medical devices to help patients in rehabilitation, as well as for self-care of deficiencies in clinics and daily life. Traditionally, these devices are produced manually, which is a time-consuming and error-prone method. From another point of view, it is possible to use imageology (X-ray or computed tomography) to scan the human body; a process that may help orthoses manufacturing but which induces radiation to the patient. To overcome this great disadvantage, several types of 3D scanners, without any kind of radiation, have emerged. This article describes the use of various types of scanners capable of digitizing the human body to produce custom orthoses. Studies have shown that photogrammetry is the most used and most suitable 3D scanner for the acquisition of the human body in 3D. With this evolution of technology, it is possible to decrease the scanning time and it will be possible to introduce this technology into clinical environment.

A potential method for sex estimation of human skeletons using deep learning and three-dimensional surface scanning

Deep learning based on radiological methods has attracted considerable attention in forensic anthropology because of its superior classification capacities over human experts. However, radiological instruments are limited in their nature of high cost and immobility. Here, we integrated a deep learning algorithm and three-dimensional (3D) surface scanning technique into a portable system for pelvic sex estimation. Briefly, the images of the ventral pubis (VP), dorsal pubis (DP), and greater sciatic notch (GSN) were cropped from virtual pelvic samples reconstructed from CT scans of 1000 individuals; 80% of them were used to train and internally evaluate convolutional neural networks (CNNs) that were then evaluated externally with the remaining samples. An additional 105 real pelvises were documented virtually with a handheld 3D surface scanner, and the corresponding snapshots of the VP, DP, and GSN were predicted by the trained CNN models. The CNN models achieved excellent performance in the external testing using CT-based images, with accuracies of 98.0%, 98.5%, and 94.0% for VP, DP, and GSN, respectively. When the CT-based models were applied to 3D scanning images, they obtained satisfactory accuracies above 95% on the VP and DP images compared to the GSN with 73.3%. In a single-blind trial, a multiple design that combined the three CNN models yielded a superior accuracy of 97.1% with 3D surface scanning images over two anthropologists. Our study demonstrates the great potential of deep learning and 3D surface scanning for rapid and accurate sex estimation of skeletal remains.

Beyond the visible spectrum - applying 3D multispectral full-body imaging to the VirtoScan system

Multispectral photography offers a wide range of applications for forensic investigations. It is commonly used to detect latent evidence and to enhance the visibility of findings. Additionally, three-dimensional (3D) full-body documentation has become much easier and more affordable in recent years. However, the benefits of performing 3D imaging beyond the visible (VIS) spectrum are not well known, and the technique has not been widely used in forensic medical investigations. A multicamera setup was used to employ multispectral photogrammetry between 365 and 960 nm in postmortem investigations. The multicamera setup included four modified digital cameras, ultraviolet (UV) and near-infrared (NIR) light sources and supplemental lens filters. Full-body documentation was performed in conjunction with the use of a medical X-ray computed tomography (CT) scanner to automate the imaging procedure. Textured 3D models based on multispectral datasets from four example cases were reconstructed successfully. The level of detail and overall quality of the 3D reconstructions varied depending on the spectral range of the image data. Generally, the NIR datasets showed enhanced visibility of vein patterns and specific injuries, whereas the UV-induced datasets highlighted foreign substances on the skin. Three-dimensional multispectral full-body imaging enables the detection of latent evidence that is invisible to the naked eye and allows visualization, documentation and analysis of evidence beyond the VIS spectrum.

Application of non-contact scanning to forensic podiatry: A feasibility study

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.

Interpol review of shoe and tool marks 2016-2019

This review paper covers the forensic-relevant literature in shoe and tool mark examination from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.

Intraoperative integration of structured light scanning for automatic tissue classification: a feasibility study

PURPOSE

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.

Determining the Effectiveness of Noncontact Three-Dimensional Surface Scanning for the Assessment of Open Injuries

Noncontact three-dimensional (3D) surface scanning methods are used within forensic medicine to record traumas and other related findings. A structured light scanning technique is one of these methods and the most suitable for the forensic field. An assessment of the efficiency of different structured light scanners with forensic injuries is essential to validate this technique for wound documentation. The purpose of this study was to evaluate the capability of the HP structured light scanner Pro S3 for digitizing open injuries having complicated areas and depths. Fifteen simulated injuries on mannequins were scanned and assessed. Comparisons between 3D and direct wound measurements were made. The results showed that the technique was able to create detailed 3D results of the extensive injuries. Statistical significance tests revealed no difference between the two measurement methods. Because the scanner is applicable for routine work, it should be considered to confirm the same results on real cadavers and actual wounds.

Virtual reality and integrated crime scene scanning for immersive and heterogeneous crime scene reconstruction

Crime scene reconstruction plays a significant role in crime solving by helping to determine the course of events. Non-invasive, high-resolution measurement and increased insight are always the goal of forensic crime scene documentation. However, entire crime scenes cannot be effectively reconstructed with traditional methods. In this study, we present a portable system that consists of a laser scanner, two hand-held structured light scanners and a low-cost virtual reality (VR) headset with a mobile power supply to conduct multi-angle and omnidirectional three-dimensional spatial data collection of crime scenes. To demonstrate practical use, a real case has been analysed to verify the feasibility and effectiveness of the system. The system accurately obtains information on decedent injuries, possible injury-inflicting tools and on-site traces. Various types of evidence from the crime scene can be jointly studied by three-dimensional visualization to develop a cohesive story. The data are presented via immersive VR rather than displayed on computer screens. The relationship between evidence chains enables us to achieve a complete crime scene reconstruction, using the specialized knowledge of experts and computer-aided forensic tools to analyse the causes of damage and identify suspects. The use of three- dimensional imaging techniques allows a more insightful survey and several useful analyses, such as accurate measurement, relative blood source location determination and injury-inflicting tool comparison.

VirtoScan-on-Rails - an automated 3D imaging system for fast post-mortem whole-body surface documentation at autopsy tables

Two-dimensional photographic documentation is a substantial part of post-mortem examinations for legal investigations. Additional three-dimensional surface documentation has been shown to assist in the visualization of findings and contribute to the reconstruction of the sequence of events. However, 2D photo documentation and, especially, 3D surface documentation, are time-consuming procedures that require specially trained personnel. In this study a 3D imaging system, called VirtoScan-on-Rails, was developed to automate and facilitate 3D surface documentation for photo documentation in autopsy suites. The imaging system was built to quickly acquire photogrammetric image sets of whole bodies during different stages of external and internal examinations. VirtoScan-on-Rails was set up in the autopsy suite of the Zurich Institute of Forensic Medicine at the University of Zurich (Zurich, Switzerland). The imaging system is based on a movable frame that carries a multi-camera array. Data quality and the applicability of the system were analyzed and evaluated within two test series. Up to 200 overlapping photographic images were acquired at consecutive image-capturing positions over a distance of approximately 2000 mm. The image-capturing process took 1 min and 23 s to acquire a set of 200 images for one side of the body. During test series one and two, 53 photogrammetric image sets taken from 31 forensic cases were successfully reconstructed. VirtoScan-on-Rails is an automated, fast and easy-to-use 3D imaging setup for autopsy suits. It facilitates documenting bodies during different stages of forensic examinations and allows standardizing the procedure of photo documentation.