X-Ray based manual Cobb angle measurement is inherently time-consuming and associated with high inter- and intra-observer variability. The existing automated scoliosis measurement methods suffer from insufficient accuracy. We propose a two-step segmentation-based deep architecture to automate Cobb angle measurement for scoliosis assessment. Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Wu Yujie, Namdar Khashayar, Chen Chaojun, Hosseinpour Shahob, Shroff Manohar, Doria Andrea, Khalvati Farzad

Keywords: Scoliosis, Deep Learning, Cobb Angle

Scoliosis is a deformity of the spine that affects 2-3% of the population. Currently, the reference standard for assessing scoliosis is the manual assignment of Cobb angles at the site of the curvature center using X-ray images. This process is time consuming and unreliable as it is affected by inter- and intra-observer variability. To overcome these inaccuracies, machine learning (ML) methods can be used to automate the Cobb angle measurement process. Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Chen Chaojun, Namdar Khashayar, Wu Yujie, Hosseinpour Shahob, Shroff Manohar, Doria Andrea, Khalvati Farzad

Keywords: Scoliosis, Cobb Angle, Deep Learning