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

## TL;DR

This paper introduces BGrowth, a semi-automatic segmentation method for vertebral fractures in MRI that achieves high accuracy and efficiency, outperforming existing techniques even with minimal manual input.

## Contribution

The paper presents a novel semi-automatic segmentation approach called BGrowth that effectively handles non-homogeneous fracture regions in vertebral MRI images.

## Key findings

- Achieved up to 95% accuracy in vertebral fracture segmentation.
- Outperformed well-known methods in literature.
- Maintained competitive processing times.

## 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).