# 3DBGrowth: volumetric vertebrae segmentation and reconstruction in magnetic resonance imaging

## TL;DR

This paper introduces 3DBGrowth, a semi-automatic method for efficient 3D vertebrae segmentation and reconstruction in MRI images, reducing manual annotation effort while improving accuracy.

## Contribution

The paper presents a novel 3D reconstruction approach based on the Balanced Growth method, incorporating slope coefficients to minimize manual annotation without sacrificing accuracy.

## Key findings

- Achieved over 5% Dice Score improvement over state-of-the-art methods.
- Reduced manual annotation to 37% of slices on average.
- Demonstrated robustness with imprecise seed points.

## Abstract

Segmentation of medical images is critical for making several processes of analysis and classification more reliable. With the growing number of people presenting back pain and related problems, the semi-automatic segmentation and 3D reconstruction of vertebral bodies became even more important to support decision making. A 3D reconstruction allows a fast and objective analysis of each vertebrae condition, which may play a major role in surgical planning and evaluation of suitable treatments. In this paper, we propose 3DBGrowth, which develops a 3D reconstruction over the efficient Balanced Growth method for 2D images. We also take advantage of the slope coefficient from the annotation time to reduce the total number of annotated slices, reducing the time spent on manual annotation. We show experimental results on a representative dataset with 17 MRI exams demonstrating that our approach significantly outperforms the competitors and, on average, only 37% of the total slices with vertebral body content must be annotated without losing performance/accuracy. Compared to the state-of-the-art methods, we have achieved a Dice Score gain of over 5% with comparable processing time. Moreover, 3DBGrowth works well with imprecise seed points, which reduces the time spent on manual annotation by the specialist.