## 1 Introduction
CNN excels at capturing local features but is unable to capture long distance dependencies. After the successful application of transformer-only architecture in the field of medical image segmentation, more and more transformer-based models have been developed to optimize semantic segmentation approaches [14]. MRI segmentation of the lumbar spine has been the subject of intensive research in recent years, with numerous approaches being proposed. The reliable detection of vertebrae from a variety of different MRI protocols and sequences remains a challenge, but at the same time, is crucial for the localization of spinal disease and subsequent treatment planning [15]. The challenge arises from the fact that different vertebrae are similar to each other, while the same vertebrae can look drastically different due to pathological deformities. Recently, reviews of different state-of-the-art methods have become available, allowing researchers to compare different neural network architectures and solutions for the segmentation of lumbar spine images, even across different imaging modalities such as CT, X-ray and MRI [16]. The use of deep learning in spinal imaging has led to many practical solutions and could have a potential future impact on research in the field of spinal imaging. These include image acquisition and reconstruction, image quality enhancement, quantitative image analysis and image interpretation [17]. Quantitative image analysis often employs the localization, labeling and segmentation of spinal structures. Image interpretation and pattern recognition have been used to detect and classify a variety of disorders, namely degenerative diseases of the lumbar spine, scoliosis, spinal tumors, spinal cord compression, cervical spondylosis and osteoporosis [17]. Recent examples of developments in spine image segmentation, detection and classification focus on lumbar spine stenosis using a CNN-based deep learning model or a CNN integrated with multiple attention mechanisms [18; 19]. To follow recent advances in neural network architecture and to test a transformer-based neural network for a medical image segmentation task, a hybrid CNN-transformer network BRAU-Net++ was used for this research [20]. Training a neural network model for medical image segmentation requires a large amount of image data with corresponding semantic segmentation masks. A large, publicly available dataset was used to train the BRAU-Net++ network for vertebral and spinal canal segmentation from sagittal MR images of the lumbar spine [21]. To evaluate the statistical significance of the different VBQ determination methods, 166 image series from 83 studies were randomly selected from the database of the resident hospital. The main objective of this study is to provide a method for automatic VBQ determination from T1-weighted sagittal MRI images of the lumbar spine. The second aim is to compare different methods of post-processing vertebral and CSF ROI and to determine whether changing the shape of the measured ROI has a statistically significant effect on the overall VBQ score. The advantage of an automated method for VBQ scoring is its ease of application to a large amount of data, making validation and comparison studies more accessible. This is particularly important as the diagnostic value of the VBQ is not well documented and the complementary role of the VBQ score in the assessment of osteoporosis remains to be evaluated [22]. The use of the VBQ score may provide exceptional health benefits by reducing patient disability and shortening the rehabilitation time of patients following surgery. It is well known that osteoporosis leads to poor surgical outcomes and that fragility fractures in orthopedic surgery are more incident in osteoporoptic bone. Postoperatively, osteoporosis is associated with an increased risk of implementation failures, extended hospitalization, a higher rate of revision surgery and higher patient mortality.