Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs

Radiographic Comparison between Cervical Spine Lateral and Whole-Spine Lateral Standing Radiographs.

Study Design Retrospective radiologic study. Objective The sagittal alignment of the cervical spine can be evaluated using either a lateral cervical radiograph or a whole-spine lateral view on which the cervical spine is included. To our knowledge, however, no report has compared the two. The purpose of this work is to identify the difference in radiographic parameters between the cervical spin...

Fully automatic segmentation and evaluation of lateral spine radiographs

Cervical spine mobility analysis on radiographs: A fully automatic approach

Conventional X-ray radiography remains nowadays the most common method to analyze spinal mobility in two dimensions. Therefore, the objective of this paper is to develop a framework dedicated to the fully automatic cervical spine mobility analysis on X-ray images. To this aim, we propose an approach based on three main steps: fully automatic vertebra detection, vertebra segmentation and angular...

Detection of Tooth caries in Bitewing Radiographs using Deep Learning

We develop a Computer Aided Diagnosis (CAD) system, which enhances the performance of dentists in detecting wide range of dental caries. The CAD System achieves this by acting as a second opinion for the dentists with way higher sensitivity on the task of detecting cavities than the dentists themselves. We develop annotated dataset of more than 3000 bitewing radiographs and utilize it for devel...

Deep metric learning for multi-labelled radiographs

Many radiological studies can reveal the presence of several co-existing abnormalities, each one represented by a distinct visual pattern. In this article we address the problem of learning a distance metric for plain radiographs that captures a notion of “radiological similarity”: two chest radiographs are considered to be similar if they share similar abnormalities. Deep convolutional neural ...