Back pain is one of the most common reasons that patients suffer and a leading cause of disability worldwide [1]. This can be divided into neck pain (cervical), middle back pain (thoracic), and lower back pain (lumbar) based on the region affected. Lower back pain is the most common type of back pain because the lumbar spine supports most of the weight of the upper body. Back pain can originate from the muscles, nerves, bones, joints or other structures of the spine [2]. Diagnostic tests can vary according to their etiology, but usually computed tomography (CT) is considered the gold standard. Nowadays, new technologies are being developing to improve the accuracy of diagnosis and decrease the workload of doctors [3].

Computer-aided diagnosis (CAD) is rapidly entering the field of radiology and has become a part of routine clinical work for medical image interpretation. The algorithms used in this area consist of several steps that may include image processing, image analysis, and classification with the use of tools, such as deep learning [4]. Deep learning allows computational models composed of multiple processing layers to learn representations of data. Vania et al. [5] used a hybrid method of convolutional neural networks (CNN) for spine segmentation CT scans and achieved good results by comparing this to other methods of spine segmentation. In our study, we developed a web-based automatic spine segmentation system from CT scans using deep learning (CNN), which has been widely used in recent years.

The segmentation process subdivides an image into its constituent parts or objects, depending on the problem to be solved. Segmentation is stopped when the region of interest in a specific application has been isolated [6]. Today, medical imaging modalities generate high resolutions and a large number of images that cannot be examined manually [7]. This is driving the development of more efficient and robust problem-tailored image analysis methods for medical imaging. Automated image segmentation could increase precision by eliminating the subjectivity of the clinician. Most of these methods consist of two steps: identification of the spine and separation of individual segments of the spine vertebrae [8].

There are several advantages to interfacing deep learning to a webpage. We aimed to develop a simple and user-friendly graphical user interface (GUI). Interfacing to a webpage makes it possible to run the method on any operating system (OS) of computers and it provides the opportunity to use any computer using an IP address if it is located on a server. Moreover, the development of the web is improving rapidly, so it will be convenient to use in the future. Deep learning can be classified as supervised and unsupervised. This system will work with the following two types of action. (1) Deep learning supervised method: Supervised learning is conducted to get a desired result (to classify) from any data. Previously prepared test data (prepared data as input and output) should be named as test data and then used to train features by computer. In comparison, unsupervised learning is conducted to get common features from previously unprepared and unclarified data. In our study, we used the supervised learning method. (2) Training specific features of data by computer takes a long time, so it is impossible to train from the very beginning. Therefore, a previously prepared hierarchical data format (HDF5) file is loaded, which makes it easier to re-train [9,10]. When training of this file is finished, it is also saved as a new one. Therefore, it is possible to use old versions. Previously trained HDF5 images are inserted and then re-training is conducted using requested data.

In this research, we aimed to develop web-based spine segmentation with deep learning using CT scans. The provided model is basically a convolutional auto-encoder, but it has skip connections from encoder layers to decoder layers that are on the same ‘level’. There is a general consensus that successful training of deep networks requires many thousands of annotated training samples. Moreover, the network must be fast. Segmentation of a 512 × 512 image takes less than a second on a recent GPU. We calculated sensitivity, dice similarity coefficient (DSC), precision, recall, and F1-score. In addition, a Bland-Altman plot was drawn with mean difference values for statistical analysis.

The automatic segmentation method was evaluated in terms of sensitivity, DSC, precision, recall, and F1-score using the data gathered [16,17]. For evaluation, we obtained true positive, true negative, false positive, and false negative results using the pixels of segmented images by automatic segmentation, and these results are shown in Table 1. The results of automatic segmentation by deep learning are shown in Figure 3. In the Bland–Altman plot, the mean difference was 1783.36, as shown in Figure 4.

Figures 5 and 6 show the user interface (UI) of a web-based system. Figure 5 shows the UI for uploading a file on a webpage. Figure 6 shows the result of segmenting the spine region through deep learning by sending the uploaded image file to the server.

In this paper, we proposed a web-based CT image segmentation system that uses deep learning. The proposed approach sends a CT image received through the web to the server, segments the spinal region through deep learning on the server, and provides the result back via the web. In this way, CT image segmentation results obtained using the GPU-based deep learning model could be provided easily and quickly anywhere on the web regardless of the specifications of the local computer. Our proposed web-based system has strong advantages in terms of scalability and accessibility. In other words, the process of entering and learning data is the same, so it is easy to expand into web-based systems for various organs or lesions. Additionally, because it is provided over the network, anyone who is allowed on the network can use the system anywhere, regardless of computer specifications, through a web browser.

However, the system requires the resolution of some problems. Although this system excluded security issues because it was developed for research purposes, the addition of security transmission modules is essential for future system deployments because medical data is delivered over the network. In addition, various additional studies and verification of deep learning-based spinal segmentation results are required. To develop a more accurate deep learning model, it is necessary to collect additional data and to select optimal parameters through comparison with various deep learning models and parameters. It is also necessary to verify the clinical significance and reliability of the model by performing multi-instrument validation through images obtained from various medical institutions and equipment. If the security issues for medical data transmission are addressed first, the web-based system proposed in this paper can be effectively used for multi-center verification.

If some problems are solved through further research and verification of the system proposed in this paper, it is expected that the various information needed by clinicians can be provided through the web through accurate spinal area segmentation in the future. It is also expected that various deep learning-based algorithms will be developed and provided via the web in accordance with clinical needs, and it is expected that they can be conveniently and easily applied for clinical use without additional purchase of expensive equipment.