# Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines
## 4 Conclusions  
Obtaining information about the vertebral bodies in the lumbar spine is very important for the diagnosis and treatment of low back pain [35]. In this study, we proposed a semi-automatic algorithm for vertebral body segmentation that requires the user to specify only a single ROI. Our method overcomes the disadvantages of the existing algorithms that require multiple user inputs, such as clicking on each vertebral body, assigning ROIs for each vertebral body, or adjusting multiple parameters depending on circumstances. In our method, only one user-defined-ROI is required to identify the ROIs for all vertebral bodies in the image. Additionally, the detected ROIs were finely adjusted using the edge-detection algorithm and Hough transformation to consider the orientation and size of the vertebral bodies. The subsequent automatic segmentation was performed by combining both the line-based and graph-cut-based methods depending on the parts of the bodies. The experimental results demonstrate comparable or an even higher performance than the existing methods, even though the automated portion of the entire segmentation process is increased. Furthermore, our method does not require a heavy training dataset necessary for deep-learning -based methods and can be applied regardless of the image size.  
In future studies, we will apply this technique to in vivo datasets and evaluate its performance for various types of contrast images obtained from living patients. The in vivo datasets may have different noise levels, field of views, contrasts, and resolutions. Motion-related artifacts and abnormality of the vertebral bodies and discs with spinal injuries or diseases should be further investigated.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Images Rotated by 10\({}^{\circ}\)**} & **Images Rotated by 20\({}^{\circ}\)** & **Images Rotated by 30\({}^{\circ}\)** \\ \hline \(\mu\) & 89.15 & 89.70 & 89.55 \\ \(\sigma\) & 3.45 & 2.96 & 3.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dice similarity coefficient (DSC) results of the additional experiments.  
Figure 7: Segmentation results for other magnetic resonance (MR) spine images. Manual segmentation results (red line). The proposed method results (green line).  
The application of dorsal vertebrae, which was not included in this study, can be a good future topic to expand and evaluate our technique. A three-dimensional (3D) extension of this technique is also one of our future studies. 3D extension of other approaches has yielded better results than those obtained via two-dimensional (2D) methods [28; 36]. Our 3D extension is also expected to improve the overall segmentation performance.  
[PERSON]; [PERSON] and [PERSON] proposed the idea and contributed to data acquisition and performed manual segmentation; [PERSON] contributed to performing data analysis, algorithm construction, and writing the article; [PERSON] technically supported the algorithm and evaluation and also professionally reviewed and edited the paper.  
This research was supported in parts by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (2016 RIA2B4015016) in support of [PERSON], and National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health in support of [PERSON] (Grant Number R01 AR066622). The contents of this paper are the sole responsibility of the authors and do not necessarily represent the official views of the sponsoring institutions.  
The authors declare no conflict of interest.