Compression Fractures Detectionon CT

被引：29

作者：

Bar, Amir ^{[1
,2
]}

Wolf, Lior ^{[1
]}

Amitai, Orna Bergman ^{[2
]}

Toledano, Eyal ^{[2
]}

Elnekave, Eldad ^{[2
]}

机构：

[1] Tel Aviv Univ, Blavatnik Sch Comp Sci, Tel Aviv, Israel

[2] Zebra Med Vis, Shefayim, Israel

来源：

MEDICAL IMAGING 2017: COMPUTER-AIDED DIAGNOSIS | 2017年 / 10134卷

关键词：

compression fracture; osteoporosis; convolutional neural networks; recurrent neural networks; VERTEBRAL FRACTURES; HIP FRACTURE; RISK; OSTEOPOROSIS; PREVALENCE; DISABILITY; MORTALITY;

D O I：

10.1117/12.2249635

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页数：8

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