## 5 Discussion  
This article presents a technique for automatically identifying kidneys' boundaries in a series of CT images generated in computed tomography examination. Diagnosis of kidney diseases is based primarily on imaging examination: ultrasound and computed tomography. Computed tomography is a much more detailed examination that gives doctors and surgeons more information, especially when planning an operation. Accurate differential diagnosis of the kidneys to identify local lesions and preoperative diagnosis to prepare for surgery requires a very careful and consistent analysis of many CT images. Since such an assessment is performed manually by specialists, tools are needed to support the image analysis process. There are many advantages to using systems to automatically assist medical diagnostics. Even an experienced person performing image analysis is prone to human errors such as fatigue, distraction, examination in uncomfortable environmental conditions. The automatic system can always generate the same response, regardless of external conditions. Another advantage of using computer tools to support the analysis of medical images is increased work efficiency. A typical set of imaging data generated in one computed tomography examination contains even several hundred images that require careful analysis. The computer system can perform this task incomparably faster. In addition to the typical support for kidney identification in CT images, there is a need for the development of new algorithms that can process data and return results in real-time (even during the examination). This allows the operator to perform the test for a quick correction of examination, e.g., to repeat the scan of a segment that does not meet the required quality standards. Computed tomography examination takes from a few to several minutes. Patient movement or even the usual displacement of the abdominal surface due to breathing can cause errors in image acquisition. The technique developed in this article can be successfully used in real-time systems due to the very high efficiency of the usage of morphological algorithms. Currently, in the literature, solutions based on machine learning are very popular. It is a valuable direction of development, and systems supported by artificial intelligence achieve very high results in the accuracy of kidney diagnosis [22; 23]. The accuracy of automatic kidney detection systems in studies reaches 86-95% [15; 24; 25]. Consideration of the data processing time during design of medical systems is particularly important when planning a real-time system implementation. The scheme developed by us is a hybrid solution that combines high accuracy with fast data processing time. In the first stage of our system, initial renal localization is carried out with the use of the U-net network. The result is generated for only one middle slice to initiate the process of kidney detection. The other slices are processed using the slice scanning algorithm technique. The result is obtained by using an extended maxima transformation. The F1-score of kidney diagnosis in our proposed system is \(84\%\pm 10\) without the use of U-Net initial support and \(89\%\pm 9\%\) with the detection of the initial search area, supported by the U-Net network. The high value of the standard deviation is due to the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Extended Maxima Transformations** & **U-Net Raw Prediction** & \\ \hline  & **Time (s)** & **Time (s)** & **Number of Slices** \\ \hline Average time for one slice & 6.12 & 18.00 & 20 \\ \hline Average time for one case & 122.39 & 360.00 & 20 \\ \hline Average time for one slice & 6.20 & 20.30 & 50 \\ \hline Average time for one case & 310.14 & 1015.00 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detailed test results of performance tests. We compared average time for one slice and for one case (20 and 50 slices). fact that the kidney sections have different sizes.