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

Author(s)

Ramos, Jonathan S. ; Watanabe, Carolina Y. V. ; Nogueira-Barbosa, Marcello H. ; Traina, Agma J. M. ; Assoc Comp Machinery

• Document type: Journal article
• Source: SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING; v. N/A, p. 8-pg., 2019-01-01.

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). (AU)

• FAPESP's process: 17/23780-2 - Content-based retrieval of medical images to aid the clinical decision using radiomics
• Grantee: Jonathan da Silva Ramos
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
• Grantee: Agma Juci Machado Traina
• Support Opportunities: Research Projects - Thematic Grants