RiFNet: Automated rib fracture detection in postmortem computed tomography.

Imaging techniques are widely used for medical diagnostics. In some cases, a lack of medical practitioners who can manually analyze the images can lead to a bottleneck. Consequently, we developed a custom-made convolutional neural network (RiFNet = Rib Fracture Network) that can detect rib fractures in postmortem computed tomography. In a retrospective cohort study, we retrieved PMCT data from 195 postmortem cases with rib fractures from July 2017 to April 2018 from our database. The computed tomography data were prepared using a plugin in the commercial imaging software Syngo.via whereby the rib cage was unfolded on a single-in-plane image reformation. Out of the 195 cases, a total of 585 images were extracted and divided into two groups labeled "with" and "without" fractures. These two groups were subsequently divided into training, validation, and test datasets to assess the performance of RiFNet. In addition, we explored the possibility of applying transfer learning techniques on our dataset by choosing two independent noncommercial off-the-shelf convolutional neural network architectures (ResNet50 V2 and Inception V3) and compared the performances of those two with RiFNet. When using pre-trained convolutional neural networks, we achieved an F1 score of 0.64 with Inception V3 and an F1 score of 0.61 with ResNet50 V2. We obtained an average F1 score of 0.91 ± 0.04 with RiFNet. RiFNet is efficient in detecting rib fractures on postmortem computed tomography. Transfer learning techniques are not necessarily well adapted to make classifications in postmortem computed tomography.

Potential use of deep learning techniques for postmortem imaging.

The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology experts have pushed researchers to develop solutions that are able to automate diagnosis by applying deep learning techniques to postmortem computed tomography images. While deep learning techniques require a good understanding of image analysis and mathematical optimization, the goal of this review was to provide to the community of postmortem radiology experts the key concepts needed to assess the potential of such techniques and how they could impact their work.

A toolbox for the rapid prototyping of crime scene reconstructions in virtual reality.

Virtual reality is recently finding its way in forensic work. The required 3D data is nowadays a standard dataset available in many cases, from homicide to traffic collisions, including not only data from the scene but also of weaponry and involved persons. Current investigations use these 3D data to replicated the incident and as discussion base for forensic personal. However, modifying the scene on a 2D viewport is often cumbersome due to the loss of the third dimension. Also to perform the modifications on the scene a 3D operator is often required. Virtual reality might improve this step by its easy use and by visualising the third dimension. This publication presents a variety of tools which can be used in forensic investigations. Additionally to the tools, examples of forensic use of these tools will be presented, showing that already a small number of tools support a variety of forensic applications.

Applying virtual reality in forensics - a virtual scene walkthrough.

A major task of forensic investigations is the documentation and interpretation of evidence to reconstruct a forensically relevant incident. To accomplish this task, a scene is documented not only with photographs but also with 3D documentation technologies. The resulting 3D data are used for 3D visualization and to perform 3D reconstructions. In this article, we present an approach for using forensic 3D data in conjunction with virtual reality to perform scene walkthroughs in the context of witness or suspect interrogations. The aim is to provide a method for scene visits showing the original scene even years after the incident. These scene walkthroughs in VR can be reproduced and allow to see through the eyes of a witness by recording their behavior and actions. These recordings allow subsequent examinations and reconstruction to support the investigation and scene understanding and can be used as evidence in court.