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

Final Pr. ID: Poster #: SCI-020

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

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

Keywords:  Scoliosis, Deep Learning, Cobb Angle

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

Final Pr. ID: Poster #: SCI-022

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

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

Keywords:  Scoliosis, Cobb Angle, Deep Learning