BGrowth: an efficient approach for the segmentation of vertebral compression fractures in magnetic resonance imaging
This work addresses the problem of automating segmentation for vertebral fractures in medical imaging, which is crucial for diagnosing back pain, but it appears incremental as it builds on existing semi-automatic approaches.
The paper tackles the challenging task of segmenting vertebral compression fractures in MRI, which is difficult due to non-homogeneous intensities and similar nearby structures, and presents the BGrowth method that achieves up to 95% accuracy on a dataset of 191 vertebrae, significantly outperforming existing methods.
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).