## 3 Results  
### The Detection of Antomies  
Tables 1 and 2 show the AP and mAP for the ROI sizes of \(64\times 64\) pixels and \(32\times 32\) pixels, respectively. The mAP for the ROIs measuring \(64\times 64\) pixels was \(0.76\pm 0.04\), which was higher than those for the ROIs measuring \(32\times 32\) pixels. Separately, for the ROIs measuring \(64\times 64\) pixels, the AP of the Nose (\(0.85\pm 0.07\)), Pons (\(0.78\pm 0.11\)), PC (\(0.74\pm 0.05\)), and AC (\(0.68\pm 0.04\)) were calculated in the higher order. There was no AP value over 0.5 for the ROIs measuring  
the same when no malformations are present. Since the object detection ability in self-driving cars need the ability to detect both cars and pedestrians from all directions, the required image features needed to be trained while involving the object itself, rather than the object plus its surroundings. With regard to the delineation of the standard lines for brain MRI, there were some discrepancies between the detection and accuracy rates. The delineation of the standard line was defined by two pairs of points of the Nose and Pons or the AC and PC, respectively (Appendix A). For this reason, the accuracy rates of the standard line were worse than the detection rates because the AP was different for each anatomical part. The difference of angles between the OM-line and the AC-PC line was reported as 12.6 degrees in another report [24]. In comparison, the accuracy rate of the standard line of our results showing less than 12 degrees was suggested to be adequate for the detection of the standard line for brain MRI. Since the angles of difference at 10 degrees on the OM-line and the AC-PC line between the original and predicted lines were 76.7 \(\pm\) 11.0 and 47.1 \(\pm\) 5.0, the delineation of the OM-line presented higher reliability for detection. The cause of the reduced accuracy rate of the AC-PC line was the lower detection accuracy on the AC because the anatomy of the AC was described as a tiny point on the sagittal image.  
The limitations of the present study are as follows. First, image training was performed only using the faster R-CNN in this study. Many kinds of network models exist for object detection using deep learning. The faster R-CNN was shown to have higher mAP in several papers [6; 25], which suggested that the mAP was one of the key indicators for the comparison of models. Though the weak point of the R-CNN was shown as the response speed of detections [25], the speed of response was less important during the acquisition of brain MRI. This study was not conducted to detect the dynamics of the anatomy. Second, the present study was only focusing on the deep learning technique. Though the model-based detection [20] was also one of the techniques for the detection of anatomies, the processing time of deep learning techniques has been improving [6; 7; 8]. We could present the processing time for the detection and delineation of the standard line within one second. However, the comparison of both techniques under the same condition should be taken into account in future research because model-based detection is also one of the robust techniques. Third, the number of training images in this study was 1200, including the images used for validation of the training. Another study [11] showed that the number of training images affected the accuracy of deep learning. Therefore, a larger number of images would improve the detection rates of the anatomy and the accuracy rates of the standard line. In this study, we focused on the detection of specific anatomical points for the delineation of the standard line of brain MRI. These results can be applied to other regions of the body and to detect tumors. The methods and results of this study will be useful for the improvement of the accuracy and will contribute to the improvement of medical image analysis, although this study focused specifically on the acquisition of brain MRI. Automatic detection for a standard line for brain MRI can help technologists improve brain MRI scans.