## 4 Experiments and Results
For the point-line distance, the experiments using the SIFT-based method obtained an average value of 2.85 mm, while experiments using the YOLO-based method obtained an average value of 0.96 mm, and hence, both methods obtained acceptable values for point-line deviation, highlighting the method based on YOLO, which obtained an average deviation of less than 1 mm. To present the accuracy of the methods, we considered correct cases where the point-line distance was below 3 mm and where the angle between lines was below 3 degrees. Cases where any of the measurements were out of the acceptable values were considered to be a detection error. With this counting of errors and successes, Table 1 shows, in each training and test group, the accuracy values (in %) for both methods considering the two metrics. Notice that, for point-line distance, the YOLO-based method had a mean accuracy of 96.0%, while the SIFT-based method had a mean accuracy of 80.57%. For the metric of line angle deviation, the YOLO-based method also had a higher mean accuracy of 96.62% compared to a mean accuracy of 80.67% obtained with the SIFT-based method. The evaluation illustrates that the algorithm is capable of accurately locating keypoints used for MRI guidance, and the automated alignment process meets the required standards for clinical use. The combination of Euclidean distance and angle difference metrics provides a comprehensive assessment of the system's performance, validating its effectiveness in automating a critical step in MRI imaging. Figure 8: Example case of line adjustment by specialist and automated methods based on SIFT and YOLO models. (**a**) Blue points and line identify the cutting plane indicated by the specialist. (**b**) Red line identifies the cutting plane automatically generated by the YOLO-based method. (**c**) Green line identifies the cutting plane automatically generated by the SIFT-based method. In terms of accuracy, the YOLO-based method outperformed all the results of the SIFT-based method, although the model training process is significantly more computationally expensive for the YOLO-based model due to the complexity of the training associated with the neural network. Considering the Google Colaboratory platform, with an Intel Xeon CPU with one core and a 2 GHz clock speed, 13 GB of RAM, 78 GB of storage, and an Nvidia Tesla T4 GPU with 15 GB of VRAM, the training time for the 10 models for cross-validation using YOLO-based neural networks took more than 5 h, which is significantly longer than training using SIFT descriptors that took only 1.50 s. It is also important to highlight that despite the significant difference in training time for the models based on SIFT and based on the YOLO neural network, the detection was done in real time in both methods once the model was already trained. Indeed, the YOLO model was able to perform tests with an average of 0.025 s per image, while tests using SIFT on the same dataset were carried out with an average of 0.133 s per image, which means that the YOLO model is almost five times faster in performing the tests. In general, even when both methods correctly detected the points, meeting the maximum deviation criterion of 3 mm, the YOLO method typically achieved detection with a smaller deviation compared to the SIFT method. Only in 13% of the successful cases did the SIFT method show a smaller deviation than the YOLO method. Figure 9 illustrates the three possible scenarios. In (a), neither method correctly detected the points indicated by the expert in yellow. In (b), both methods succeeded, with YOLO demonstrating higher accuracy--represented by the red line. In (c), both methods succeeded, with SIFT showing higher accuracy--represented by the blue line. To compare the robustness of the methods regarding image quality, the presence of noise in each image was estimated using the Signal-to-Noise Ratio (SNR).