Anatomical Structure Sketcher for Cephalograms by Bimodal Deep Learning

The lateral cephalogram is a commonly used medium to acquire patient-specific morphology for diagnose and treatment planning in clinical dentistry. The robust anatomical structure detection and accurate annotation remain challenging considering the personal skeletal variations and image blurs caused by device-specific projection magnification, together with structure overlapping in the lateral cephalograms. We propose a novel cephalogram sketcher system, where the contour extraction of anatomical structures is formulated as a cross-modal morphology transfer from regular image patches to arbitrary curves. Specifically, the image patches of structures of interest are located by a hierarchical pictorial model. The automatic contour sketcher converts the image patch to a morphable boundary curve via a bimodal deep Boltzmann machine. The deep machine learns a joint representation of patch textures and contours, and forms a path from one modality (patches) to the other (contours). Thus, the sketcher can infer the contours by alternating Gibbs sampling along the path in a manner similar to the data completion. The proposed method is robust not only to structure detection, but also tends to produce accurate structure shapes and landmarks even in blurry X-ray images. The experiments performed on clinically captured cephalograms demonstrate the effectiveness of our method.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000346352700099&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Artificial IntelligenceCPCI-S(ISTP)

Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks

Background: Three-dimensional (3D) cephalometric analysis using computerized tomography data has been rapidly adopted for dysmorphosis and anthropometry. Several different approaches to automatic 3D annotation have been proposed to overcome the limitations of traditional cephalometry. The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation. Methods: To overcome current technical limitations, some measures were developed to directly annotate 3D human skull data. Our deep learning-based model system mainly consisted of a 3D convolutional neural network and image data resampling. Results: The discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to evaluate system accuracy. Our new model system yielded prediction errors of 3.26, 3.18, and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no difference among the landmarks of the three groups, including the midsagittal plane, horizontal plane, and mandible (p>0.05). Conclusion: A new 3D convolutional neural network-based automatic annotation system for 3D cephalometry was developed. The strategies used to implement the system were detailed and measurement results were evaluated for accuracy. Further development of this system is planned for full clinical application of automatic 3D cephalometric annotation