Literature DB >> 26878138

A multi-center milestone study of clinical vertebral CT segmentation.

Jianhua Yao1, Joseph E Burns2, Daniel Forsberg3, Alexander Seitel4, Abtin Rasoulian4, Purang Abolmaesumi4, Kerstin Hammernik5, Martin Urschler6, Bulat Ibragimov7, Robert Korez7, Tomaž Vrtovec7, Isaac Castro-Mateos8, Jose M Pozo8, Alejandro F Frangi8, Ronald M Summers1, Shuo Li9.   

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.

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Year:  2016        PMID: 26878138      PMCID: PMC5527557          DOI: 10.1016/j.compmedimag.2015.12.006

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  23 in total

1.  A deformable-model approach to semi-automatic segmentation of CT images demonstrated by application to the spinal canal.

Authors:  Stuart S C Burnett; George Starkschalla; Craig W Stevens; Zhongxing Liao
Journal:  Med Phys       Date:  2004-02       Impact factor: 4.071

2.  A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine.

Authors:  André Mastmeyer; Klaus Engelke; Christina Fuchs; Willi A Kalender
Journal:  Med Image Anal       Date:  2006-07-07       Impact factor: 8.545

3.  Automated model-based vertebra detection, identification, and segmentation in CT images.

Authors:  Tobias Klinder; Jörn Ostermann; Matthias Ehm; Astrid Franz; Reinhard Kneser; Cristian Lorenz
Journal:  Med Image Anal       Date:  2009-02-20       Impact factor: 8.545

4.  Lumbar spine segmentation using a statistical multi-vertebrae anatomical shape+pose model.

Authors:  Abtin Rasoulian; Robert Rohling; Purang Abolmaesumi
Journal:  IEEE Trans Med Imaging       Date:  2013-06-12       Impact factor: 10.048

5.  Automatic inference of articulated spine models in CT images using high-order Markov Random Fields.

Authors:  Samuel Kadoury; Hubert Labelle; Nikos Paragios
Journal:  Med Image Anal       Date:  2011-02-12       Impact factor: 8.545

Review 6.  The evolution of image-guided lumbosacral spine surgery.

Authors:  Austin C Bourgeois; Austin R Faulkner; Alexander S Pasciak; Yong C Bradley
Journal:  Ann Transl Med       Date:  2015-04

7.  Shape representation for efficient landmark-based segmentation in 3-d.

Authors:  Bulat Ibragimov; Boštjan Likar; Franjo Pernuš; Tomaž Vrtovec
Journal:  IEEE Trans Med Imaging       Date:  2014-04       Impact factor: 10.048

8.  A biomechanical model for estimating loads on thoracic and lumbar vertebrae.

Authors:  Sravisht Iyer; Blaine A Christiansen; Benjamin J Roberts; Michael J Valentine; Rajaram K Manoharan; Mary L Bouxsein
Journal:  Clin Biomech (Bristol, Avon)       Date:  2010-07-23       Impact factor: 2.063

9.  Fully automatic measurements of axial vertebral rotation for assessment of spinal deformity in idiopathic scoliosis.

Authors:  Daniel Forsberg; Claes Lundström; Mats Andersson; Ludvig Vavruch; Hans Tropp; Hans Knutsson
Journal:  Phys Med Biol       Date:  2013-02-26       Impact factor: 3.609

10.  An improved level set method for vertebra CT image segmentation.

Authors:  Juying Huang; Fengzeng Jian; Hao Wu; Haiyun Li
Journal:  Biomed Eng Online       Date:  2013-05-28       Impact factor: 2.819

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  22 in total

1.  Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.

Authors:  Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana
Journal:  Acad Radiol       Date:  2019-08-10       Impact factor: 3.173

2.  Landmark-guided diffeomorphic demons algorithm and its application to automatic segmentation of the whole spine and pelvis in CT images.

Authors:  Shouhei Hanaoka; Yoshitaka Masutani; Mitsutaka Nemoto; Yukihiro Nomura; Soichiro Miki; Takeharu Yoshikawa; Naoto Hayashi; Kuni Ohtomo; Akinobu Shimizu
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-11-30       Impact factor: 2.924

Review 3.  Image-guidance technology and the surgical resection of spinal column tumors.

Authors:  Bhargav Desai; Jonathan Hobbs; Grant Hartung; Guoren Xu; Ziya L Gokaslan; Andreas Linninger; Ankit I Mehta
Journal:  J Neurooncol       Date:  2016-11-28       Impact factor: 4.130

4.  Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Authors:  Bulat Ibragimov; Lei Xing
Journal:  Med Phys       Date:  2017-02       Impact factor: 4.071

5.  A Region-Based Deep Level Set Formulation for Vertebral Bone Segmentation of Osteoporotic Fractures.

Authors:  Faisal Rehman; Syed Irtiza Ali Shah; M Naveed Riaz; S Omer Gilani; Faiza R
Journal:  J Digit Imaging       Date:  2020-02       Impact factor: 4.056

6.  A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation.

Authors:  Danis Alukaev; Semen Kiselev; Tamerlan Mustafaev; Ahatov Ainur; Bulat Ibragimov; Tomaž Vrtovec
Journal:  Eur Spine J       Date:  2022-05-21       Impact factor: 2.721

7.  Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning.

Authors:  Zhongyi Han; Benzheng Wei; Stephanie Leung; Ilanit Ben Nachum; David Laidley; Shuo Li
Journal:  Neuroinformatics       Date:  2018-10

8.  Automated Abdominal CT Imaging Biomarkers for Opportunistic Prediction of Future Major Osteoporotic Fractures in Asymptomatic Adults.

Authors:  Perry J Pickhardt; Peter M Graffy; Ryan Zea; Scott J Lee; Jiamin Liu; Veit Sandfort; Ronald M Summers
Journal:  Radiology       Date:  2020-08-11       Impact factor: 11.105

9.  Spinal pedicle screw planning using deformable atlas registration.

Authors:  J Goerres; A Uneri; T De Silva; M Ketcha; S Reaungamornrat; M Jacobson; S Vogt; G Kleinszig; G Osgood; J-P Wolinsky; J H Siewerdsen
Journal:  Phys Med Biol       Date:  2017-02-08       Impact factor: 4.174

10.  Automatic pedicle screw planning using atlas-based registration of anatomy and reference trajectories.

Authors:  R Vijayan; T De Silva; R Han; X Zhang; A Uneri; S Doerr; M Ketcha; A Perdomo-Pantoja; N Theodore; J H Siewerdsen
Journal:  Phys Med Biol       Date:  2019-08-21       Impact factor: 4.174

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