Automated detection of bone splinters in DEXA phantoms using deep neural networks

Abstract

Dual energy radiographic imaging is a method to provide material information and can be used to differentiate between various tissue types. Dual energy X-ray absorption (DEXA) can be applied for breast density, osteoporosis or bone fracture analysis. To support radiologists with the assessment of DEXA images, machine learning can be applied. Specifically, deep convolutional neural networks (DCNNs) can be used for medical image analysis. In this work a DCNN is proposed and evaluated for automated detection of bone splinters in DEXA phantom images. The image data consists of 47 phantoms with (35) and without (12) bone splinters. Material decomposition and energy weighting results in additional image channels. Various DCNN architectures and parameters were explored. A classification rate in regions with 90 % and without 99 % bone splinters was achieved.