  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.
