2020/04/04 by Sandor Konya, S. Kónya, Konya, Sandor +10
Computer Science · Dentistry · Engineering · #Advanced X-ray and CT Imaging #Computer Vision and Pattern Recognition (cs.CV) #Dental Radiography and Imaging #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Imaging and Analysis #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2004.03364
Submitted to Medical & Biological Engineering & Computing
arxiv created 2020/04/04 · openalex publication_date 2020/04/04 · arxiv updated 2020/04/08 · openalex created_date 2020/04/17 · openalex updated_date 2026/07/28
The aim of this study is to investigate the segmentation accuracies of different segmentation networks trained on 730 manually annotated lateral lumbar spine X-rays. Instance segmentation networks were compared to semantic segmentation networks. The study cohort comprised diseased spines and postoperative images with metallic implants. The average mean accuracy and mean intersection over union (IoU) was up to 3 percent better for the best performing instance segmentation model, the average pixel accuracy and weighted IoU were slightly better for the best performing semantic segmentation model. Moreover, the inferences of the instance segmentation models are easier to implement for further processing pipelines in clinical decision support.