Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Yao, J; Burns, JE; Forsberg, D; Seitel, A; Rasoulian, A; Abolmaesumi, P; Hammernik, K; Urschler, M; Ibragimov, B; Korez, R; Vrtovec, T; Castro-Mateos, I; Pozo, JM; Frangi, AF; Summers, RM; Li, S.
A multi-center milestone study of clinical vertebral CT segmentation.
Comput Med Imaging Graph. 2016; 49(4):16-28
Doi: 10.1016/j.compmedimag.2015.12.006
[OPEN ACCESS]
Web of Science
PubMed
FullText
FullText_MUG
- Co-Autor*innen der Med Uni Graz
- Urschler Martin
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Scite (citation analytics):
- Abstract:
- A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention. Copyright © 2016 Elsevier Ltd. All rights reserved.
- Find related publications in this database (using NLM MeSH Indexing)
- Aged -
- Aged, 80 and over -
- Algorithms -
- California -
- Female -
- Humans -
- Lumbar Vertebrae - diagnostic imaging
- Male -
- Middle Aged -
- Pattern Recognition, Automated - methods
- Radiographic Image Enhancement - methods
- Radiographic Image Interpretation, Computer-Assisted - methods
- Reference Values -
- Reproducibility of Results -
- Sensitivity and Specificity -
- Software Validation -
- Subtraction Technique -
- Thoracic Vertebrae - diagnostic imaging
- Tomography, X-Ray Computed - methods
- Tomography, X-Ray Computed - standards
- Tomography, X-Ray Computed - statistics & numerical data