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

Computer scienceVertebral compression fractureCompression (physics)Convolutional neural networkFracture (geology)Artificial intelligenceSagittal planeOsteoporosisData compressionPattern recognition (psychology)RadiologyMedicineMaterials science

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Year
2017
Type
article
Volume
10134
Pages
1013440-1013440
Citations
63
Access
Closed

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Amir Bar, Lior Wolf, Orna Bergman Amitai et al. (2017). Compression fractures detection on CT. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE , 10134 , 1013440-1013440. https://doi.org/10.1117/12.2249635

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DOI
10.1117/12.2249635