## 1 Introduction  
Osteoporosis is a systemic bone disease that causes degeneration of bone tissue microstructures and low bone quality [1, 2]. Therefore, the assessment of bone mineral density (BMD) in patients undergoing spinal surgery is important for clinical decision making [3, 4]. BMD is determined using dual-energy X-ray absorptiometry (DEXA). This technique is recommended by the World Health Organization (WHO) as the diagnostic tool of choice for osteoporosis screening [5]. The main problem with DEXA and BMD assessment is the relatively low screening rate in the majority of patient groups. Some surveys found that only 44% of spine surgeons routinely obtained DEXA results prior to instrumental fusion, although DEXA screening prior to surgical instrumentation is generally advocated for [6, 7]. The importance of assessing bone quality in the preoperative treatment of patients is recognized as a prerequisite for a positive outcome of vertebosynthesis procedures and the avoidance of postoperative complications. The gold standard for BMD assessment is DEXA. However, it overestimates BMD in patients with degenerative changes in the spine and in patients with excess body weight [8]. In view of this, there are alternative radiological methods to measure BMD using MRI as a non-invasive routine examination prior to surgical procedures. Recent research has shown that it is possible to calculate BMD valuesobtained through MRI examination. However, the correlation of BMD obtained by MRI examinations with the values obtained by DEXA needs to be confirmed so that this method can be included in the preoperative treatment of patients undergoing vertebosynthesis. Deformities of the spine, atherosclerotic changes in the aorta and previous compression fractures of the vertebrae lead to increased X-ray absorption and falsely increase the T value [9,10]. Recent studies show that measuring the fat content in the cancellous bone on a T1-weighted image using MRI is a more sensitive method of assessing BMD and can predict the occurrence of vertebral fracture much more accurately than DEXA [6,11]. The vertebral bone quality (VBQ) score is a new scoring method in magnetic resonance imaging (MRI) that can be used to improve screening for osteoporosis [6,12]. The VBQ score can be determined from standard clinical, non-contrast T1-weighted MR images without additional software. The advantage of this method is that it does not require ionizing radiation, is easy to use, and does not require additional equipment or special clinical protocols. The proposed method measures the signal intensity (SI) of vertebrae and cerebrospinal fluid (CSF) in specific regions of interest (ROIs). The ROIs are usually drawn manually by the radiologist, which can jeopardize the consistency of the results obtained. This can be improved by automating the ROI determination. Therefore, automating the VBQ determination may drastically reduce the time required to yield plausible results. Furthermore, it does not require any additional work from a radiologist, except for final monitoring. Although the automatic segmentation of anatomical features can be performed using analytical algorithms such as connected threshold or region growing, most of these methods require user input in addition to the medical images. Usually, this input consists of the coordinates of initial seed points from which the algorithm starts the calculation. An advanced method is the use of a trained neural network model for the semantic segmentation of medical images. The use of machine learning and deep learning in conjunction with computer vision has improved the interpretation of medical images and made these techniques an important tool in healthcare [13]. Currently, the segmentation model-based convolutional neural network (CNN) is used for most medical image segmentation applications, with U-Net being one of the most widely used models. The encoder-decoder design of U-Net combines information at multiple scales and improves the performance of the segmentation model.