## 4 Experiments and Results  
In order to present the experimental results for the proposed methods, a public dataset was used to extract sagittal plane images for training and testing the models. The International Neuroimaging Data-sharing Initiative (INDI) [31] provides a public dataset with volumetric brain MRI data from witch sagittal plane images can be obtained for training and testing the models. A total of 150 MR images of the sagittal plane were considered for experiments with both SIFT- and YOLO-based methods. A 10-fold cross-validation was considered in order to present mean accuracy. The dataset was divided into 10 folds. At each iteration, nine parts (or 90%) were used to train the model and one part (or 10%) to test it. This procedure was repeated 10 times so that each of the 10 parts was used as a test set once, ensuring that the model was evaluated on different subsets of the dataset. In order to evaluate the ability of the algorithm to locate the desired keypoints in the test image during the matching step, characteristic points that are normally used as guidance for the MRI operator during the exam have been chosen. These points are critical for ensuring accurate alignment and imaging during MRI scans. The system is designed to automatically find these points, which are essential for the subsequent steps in the image analysis process. The characteristic points selected for this evaluation are the two bottom extremities of the corpus callosum. In a typical manual process, the MRI operator needs to align a line passing through these points to ensure correct imaging orientation. This alignment is crucial for obtaining high-quality images that are correctly oriented and aligned with anatomical structures. By automating this process, the system aims to reduce the operator's workload and improve the consistency and accuracy of the alignment. To automatically detect these points, both methods using SIFT and YOLO were considered to present a performance comparison. These points were used to perform line adjustment to align a line passing through the detected points. This automatic alignment mimics the manual process performed by the operator. To measure the effectiveness of the system, two metrics were chosen. The first metric is the Euclidean distance between the points marked manually and the line detected auto  
Figure 7: Details for the YOLO-based method for line adjustment. In training phase, annotated images are used to build the neural network model. In the fitting phase, a not annotated image is shown to the model to find keypoints to define cutting plane for automated exam adjustment. matically. This distance is measured in millimeters and provides a quantitative assessment of the accuracy of the automatically detected points relative to the manually marked points. A smaller distance indicates a higher level of accuracy and a better match between the manual and automatic processes. The second metric is the difference between the angles of the manual marked line and the automatically detected line. The angle difference is measured in degrees and provides an additional measure of the alignment accuracy. A smaller angle difference indicates that the automatically detected points result in a line that is closely aligned with the manually marked line, further validating the effectiveness of the methods. As an example, Figure 8 illustrates the final line detection using both methods. In (a), blue points and line identify the cutting plane indicated by the specialist. In (b), red line identifies the cutting plane automatically generated by the YOLO-based method. In (c), green line identifies the cutting plane automatically generated by the SIFT-based method. According to the literature, deviation limits of 3 mm for the point-line distance and 3 degrees for the difference in the angle of the lines are acceptable, as referenced in [32].