@inproceedings{629c077179ad4c38a053ad8d79ffcb1a,
title = "Compression fractures detection on CT",
abstract = "The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. Less than one third of vertebral compression fractures are diagnosed clinically. We present an automated method for detecting spine compression fractures in Computed Tomography (CT) scans. The algorithm is composed of three processes. First, the spinal column is segmented and sagittal patches are extracted. The patches are then binary classified using a Convolutional Neural Network (CNN). Finally a Recurrent Neural Network (RNN) is utilized to predict whether a vertebral fracture is present in the series of patches.",
keywords = "Compression fracture, Convolutional neural networks, Osteoporosis, Recurrent neural networks",
author = "Amir Bar and Lior Wolf and {Bergman Amitai}, Orna and Eyal Toledano and Eldad Elnekave",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; Medical Imaging 2017: Computer-Aided Diagnosis ; Conference date: 13-02-2017 Through 16-02-2017",
year = "2017",
doi = "10.1117/12.2249635",
language = "الإنجليزيّة",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Petrick, {Nicholas A.} and Armato, {Samuel G.}",
booktitle = "Medical Imaging 2017",
address = "الولايات المتّحدة",
}