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

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.Comment: This is a pre-print of an article published in Computer-Based Medical Systems. The final authenticated version is available online at: https://doi.org/10.1109/CBMS.2019.0009

Vertebral body segmentation of spine MR images using superpixels

This paper presents a segmentation approach guided by the user for extracting the vertebral bodies of spine from MRI. The proposed approach, called VBSeg, takes advantage of superpixels to reduce the image complexity and then making easy the detection of each vertebral body contour. Superpixels adapt themselves to the image structures, once their formation law follows the homogeneity of the image regions. However, for some diseases or abnormalities, the boundary of each superpixel does not fit well in the vertebra contour. To avoid this drawback, we propose to use the Otsu’s method as a pos-segmentation step to divide the superpixels into smaller ones. The final segmentation is obtained through a region growing approach using points manually selected by the specialist. It can produce masks of the five lumbar vertebrae with an average precision of 80% and recall of 87%, when compared to the manual segmentation of a trained specialist. These values show that the VBSeg is a valuable asset to assist the medical specialist in the task of vertebral bodies’ segmentation, with much less effort and time demand.FAPESPCAPESSticAMSUDRESCUER project, funded by the European Commission (Grant 614154) and by the Brazilian National Council for Scientific and Technological Development CNPq/MCT