Pelvic fracture is one of the leading causes of death in the elderly, carrying a high risk of death within 1 year of fracture. This study proposes an automated method to detect pelvic fractures on 3-dimensional computed tomography (3D-CT). Deep convolutional neural networks (DCNNs) have been used for lesion detection on 2D and 3D medical images. However, training a DCNN directly using 3D images is complicated, computationally costly, and requires large amounts of training data. We propose a method that evaluates multiple, 2D, real-time object detection systems (YOLOv3 models) in parallel, in which each YOLOv3 model is trained using differently orientated 2D slab images reconstructed from 3D-CT. We assume that an appropriate reconstruction orientation would exist to optimally characterize image features of bone fractures on 3D-CT. Multiple YOLOv3 models in parallel detect 2D fracture candidates in different orientations simultaneously. The 3D fracture region is then obtained by integrating the 2D fracture candidates. The proposed method was validated in 93 subjects with bone fractures. Area under the curve (AUC) was 0.824, with 0.805 recall and 0.907 precision. The AUC with a single orientation was 0.652. This method was then applied to 112 subjects without bone fractures to evaluate over-detection. The proposed method successfully detected no bone fractures in all except 4 non-fracture subjects (96.4%).
Facial recognition has been employed as a user-friendly person authentication method, and facial spoofing attack has become a common problem. Although there are two kinds of facial spoofing attacks, 2D spoofing and 3D spoofing, almost studies evaluate the performance only for 2D spoofing. Temporal change of face color will be a possible characteristic to detect liveness against to 3D spoofing attack because there is a relationship between the skin blood perfusion change and the temporal color change in facial video images. This paper proposes two features, R-G correlation feature and interarea correlation feature, to detect liveness using video images. Also, liveness detection method using support vector machine is demonstrated. The performance was evaluated by accuracy (ACC) for classifying liveness face and three types of spoofing face-2D printed image, 2D monitor image, and 3D doll. The ACC was 99.2% at the lighting condition of room light, 99.5% at sunlight illuminating the face, and 98.6% at sunlight illuminating the back of the head.
K E Y W O R D S3D spoofing, facial recognition, facial skin blood perfusion change, liveness detection 42
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.