Peter Bernstein1, Johannes Metzler2, Marlene Weinzierl3, Carl Seifert3, Wadim Kisel3, Markus Wacker2. 1. Department for Orthopaedics and Traumatology, University Comprehensive Spine Center, University Hospital Dresden, Fetscherstrasse 74, 01307, Dresden, Germany. bernspe@gmail.com. 2. Faculty of Informatics/Mathematics, HTW Dresden, Friedrich-List-Platz 1, 01069, Dresden, Germany. 3. Department for Orthopaedics and Traumatology, University Comprehensive Spine Center, University Hospital Dresden, Fetscherstrasse 74, 01307, Dresden, Germany.
Abstract
INTRODUCTION AND OBJECTIVE: Although being standard for scoliosis curve size estimation, COBB angle measurement is well known to be inaccurate, due to a high interobserver variance in end vertebra selection and end plate contour delineation. We propose a stepwise improvement by using a spline constructed from vertebra centroids to resemble spinal curve characteristics more closely. To enhance precision even further, a neural net was trained to detect the centroids automatically. MATERIALS & METHODS: Vertebra centroids in AP spinal X-ray images of varying quality from 551 scoliosis patients were manually labeled by 4 investigators. With these inputs, splines were generated and the computed curve sizes were compared to the manually measured COBB angles and to the curve estimation obtained from the neural net. RESULTS: Splines achieved a higher interobserver correlation of 0.92-0.95 compared to manual COBB measurements (0.83-0.92) and showed 1.5-2 times less variance, depending on the anatomic region. This translates into an average of 1° of interobserver measurement deviation for spline-based curve estimation compared to 3°-8° for COBB measurements. The neural net was even more precise and achieved mean deviations below 0.5°. CONCLUSION: In conclusion, our data suggest an advantage of spline-based automated measuring systems, so further investigations are warranted to abandon manual COBB measurements.
INTRODUCTION AND OBJECTIVE: Although being standard for scoliosis curve size estimation, COBB angle measurement is well known to be inaccurate, due to a high interobserver variance in end vertebra selection and end plate contour delineation. We propose a stepwise improvement by using a spline constructed from vertebra centroids to resemble spinal curve characteristics more closely. To enhance precision even further, a neural net was trained to detect the centroids automatically. MATERIALS & METHODS: Vertebra centroids in AP spinal X-ray images of varying quality from 551 scoliosispatients were manually labeled by 4 investigators. With these inputs, splines were generated and the computed curve sizes were compared to the manually measured COBB angles and to the curve estimation obtained from the neural net. RESULTS: Splines achieved a higher interobserver correlation of 0.92-0.95 compared to manual COBB measurements (0.83-0.92) and showed 1.5-2 times less variance, depending on the anatomic region. This translates into an average of 1° of interobserver measurement deviation for spline-based curve estimation compared to 3°-8° for COBB measurements. The neural net was even more precise and achieved mean deviations below 0.5°. CONCLUSION: In conclusion, our data suggest an advantage of spline-based automated measuring systems, so further investigations are warranted to abandon manual COBB measurements.
Authors: Claudius Thomé; Peter Douglas Klassen; Gerrit Joan Bouma; Adisa Kuršumović; Javier Fandino; Martin Barth; Mark Arts; Wimar van den Brink; Richard Bostelmann; Aldemar Hegewald; Volkmar Heidecke; Peter Vajkoczy; Susanne Fröhlich; Jasper Wolfs; Richard Assaker; Erik Van de Kelft; Hans-Peter Köhler; Senol Jadik; Sandro Eustacchio; Robert Hes; Frederic Martens Journal: Spine J Date: 2018-05-03 Impact factor: 4.166
Authors: Dean N Papaliodis; Pierino G Bonanni; Timothy T Roberts; Khalid Hesham; Nicholas Richardson; Robert A Cheney; James P Lawrence; Allen L Carl; William F Lavelle Journal: Int J Spine Surg Date: 2017-06-30