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arXiv · 1906.08620

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

Jonathan S. Ramos·Carolina Y. V. Watanabe·Marcello H. Nogueira-Barbosa·Agma J. M. Traina
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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-artmethods. Moreover, BGrowth presents the best results even with a rough (sloppy) manual annotation (seed points).

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Jonathan S. Ramos, Carolina Y. V. Watanabe, Marcello H. Nogueira-Barbosa, Agma J. M. Traina. 2019-06-25. BGrowth: an efficient approach for the segmentation of vertebral compression fractures in magnetic resonance imaging. https://doi.org/10.1145/3297280.3299728

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