research-article

BGrowth: an efficient approach for the segmentation of vertebral compression fractures in magnetic resonance imaging

Authors:

Jonathan S. Ramos,

Carolina Y. V. Watanabe,

Marcello H. Nogueira-Barbosa,

Agma J. M. TrainaAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 220 - 227

https://doi.org/10.1145/3297280.3299728

Published: 08 April 2019 Publication History

Abstract

Segmentation of medical images is a critical issue: several process of analysis and classification rely on this segmentation. With the growing number of people presenting back pain and problems related to it, the automatic or semi-automatic segmentation of fractured vertebral bodies became a challenging task. In general, those fractures present several regions with non-homogeneous intensities and the dark regions are quite similar to the structures nearby. Aimed at overriding this challenge, in this paper we present a semi-automatic segmentation method, called Balanced Growth (BGrowth). The experimental results on a dataset with 102 crushed and 89 normal vertebrae show that our approach significantly outperforms well-known methods from the literature. We have achieved an accuracy up to 95% while keeping acceptable processing time performance, that is equivalent to the state-of-the-art methods. Moreover, BGrowth presents the best results even with a rough (sloppy) manual annotation (seed points).

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Cited By

Chiari-Correia NNogueira-Barbosa MChiari-Correia RAzevedo-Marques P(2023)A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRIJournal of Digital Imaging10.1007/s10278-023-00847-436:4(1565-1577)Online publication date: 30-May-2023
https://doi.org/10.1007/s10278-023-00847-4
Whyne CFerguson DClement ARangrez MHardisty M(2020)Biomechanical Properties of Metastatically Involved Osteolytic BoneCurrent Osteoporosis Reports10.1007/s11914-020-00633-z18:6(705-715)Online publication date: 19-Oct-2020
https://doi.org/10.1007/s11914-020-00633-z
Chettrit DMeir TLebel HOrlovsky MGordon RAkselrod-Ballin ABar A(2020)3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CTMedical Image Computing and Computer Assisted Intervention – MICCAI 202010.1007/978-3-030-59725-2_72(743-752)Online publication date: 4-Oct-2020
https://dl.acm.org/doi/10.1007/978-3-030-59725-2_72
Show More Cited By

Index Terms

BGrowth: an efficient approach for the segmentation of vertebral compression fractures in magnetic resonance imaging
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation

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Published In

cover image ACM Conferences

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2019

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Fundação de Amparo à Pesquisa do Estado de São Paulo
Coordination for the Improvement of Higher Level -or Education- Personnel
Conselho Nacional de Desenvolvimento Científico e Tecnológico

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SAC '19

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SIGAPP

SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2019

Limassol, Cyprus

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Chiari-Correia NNogueira-Barbosa MChiari-Correia RAzevedo-Marques P(2023)A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRIJournal of Digital Imaging10.1007/s10278-023-00847-436:4(1565-1577)Online publication date: 30-May-2023
https://doi.org/10.1007/s10278-023-00847-4
Whyne CFerguson DClement ARangrez MHardisty M(2020)Biomechanical Properties of Metastatically Involved Osteolytic BoneCurrent Osteoporosis Reports10.1007/s11914-020-00633-z18:6(705-715)Online publication date: 19-Oct-2020
https://doi.org/10.1007/s11914-020-00633-z
Chettrit DMeir TLebel HOrlovsky MGordon RAkselrod-Ballin ABar A(2020)3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CTMedical Image Computing and Computer Assisted Intervention – MICCAI 202010.1007/978-3-030-59725-2_72(743-752)Online publication date: 4-Oct-2020
https://dl.acm.org/doi/10.1007/978-3-030-59725-2_72
Ramos JCazzolato MFaical BLinares ONogueira-Barbosa MTraina CTraina A(2019)Fast and Smart Segmentation of Paraspinal Muscles in Magnetic Resonance Imaging with CleverSeg2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI.2019.00019(76-83)Online publication date: Oct-2019
https://doi.org/10.1109/SIBGRAPI.2019.00019
Ramos JCazzolato MFaical BNogueira-Barbosa MTraina CTraina A(2019)3DBGrowth: Volumetric Vertebrae Segmentation and Reconstruction in Magnetic Resonance Imaging2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS.2019.00091(435-440)Online publication date: Jun-2019
https://doi.org/10.1109/CBMS.2019.00091

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