Computer aided detection for vertebral deformities diagnosis based on deep learning

Abstract

<p class="p1">The diagnosis of spinal deformities is one of the most frequent daily clinical routine. X-ray images are used to diagnose s<span class="s1">e</span><span class="s2">v</span>eral pathologies in order to reduce harmful radiat<span class="s2">i</span>ons of the patient. Spinal deformities are diagnosed essentially from <span class="s2">v</span>ertebral shapes, orientations, and positions, so their detection and s<span class="s2">e</span>gmentation are major steps required for diagnosis. Deep learning could be applied for automatic diagnosis to detect scoliosis and its <span class="s1">v</span>ariants with a <span class="s2">f</span><span class="s1">av</span>ourable performance. In this stud<span class="s3">y</span>, based on 609 spinal anterio<span class="s1">r</span>-posterior x-ray images obtained from the public Spine<span class="s4">W</span>eb, we <span class="s2">e</span>xamine generat<span class="s1">i</span><span class="s2">v</span>e ad- <span class="s2">v</span>ersarial net<span class="s2">w</span>ork (GAN) based architectures and co<span class="s5">n</span><span class="s1">v</span>olutional neural net<span class="s2">w</span>ork (CNN) based architectures models that are capable of automatically detecting anomalies in radiograph and achi<span class="s1">e</span><span class="s2">v</span>e <span class="s2">e</span>xpert-l<span class="s1">e</span><span class="s2">v</span>el performances in <span class="s1">v</span>arious fi<span class="s6">e</span>lds pr<span class="s2">o</span>viding a solid comparat<span class="s1">i</span><span class="s2">v</span>e stud<span class="s3">y</span>. Most of the implemented models are apt to automatically distinguish limits between <span class="s2">v</span>ertebrae so determining their shape with a <span class="s2">v</span>ery good visual performance. The GAN-based archite<span class="s2">c</span>ture estimates the required <span class="s2">v</span>ertebral landmarks with an accura<span class="s2">c</span>y rate of 0.966, signify its capacity for automatic scoliosis assessment in a clinical setting.</p>