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Proceedings Paper

Compression fractures detection on CT
Author(s): Amir Bar; Lior Wolf; Orna Bergman Amitai; Eyal Toledano; Eldad Elnekave
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Paper 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.

Paper Details

Date Published: 3 March 2017
PDF: 8 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013440 (3 March 2017); doi: 10.1117/12.2249635
Show Author Affiliations
Amir Bar, Tel Aviv Univ. (Israel)
Zebra Medical Vision, Inc. (Israel)
Lior Wolf, Tel Aviv Univ. (Israel)
Orna Bergman Amitai, Zebra Medical Vision, Inc. (Israel)
Eyal Toledano, Zebra Medical Vision, Inc. (Israel)
Eldad Elnekave, Zebra Medical Vision, Inc. (Israel)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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