Real-Time Automatic Configuration of Brain MRI: A Comparative Study of SIFT Descriptors and YOLO Neural Network

This work presents two approaches to image processing in brain magnetic resonance imaging (MRI) to enhance slice planning during examinations. The first approach involves capturing images from the operator’s console during slice planning for two different brain examinations. From these images, Scale-Invariant Feature Transform (SIFT) descriptors are extracted from the regions of interest. These descriptors are then utilized to train and test a model for image matching. The second approach introduces a novel method based on the YOLO (You Only Look Once) neural network, which is designed to automatically align and orient cutting planes. Both methods aim to automate and assist operators in decision making during MRI slice planning, thereby reducing human dependency and improving examination accuracy. The SIFT-based method demonstrated satisfactory results, meeting the necessary requirements for accurate brain examinations. Meanwhile, the YOLO-based method provides a more advanced and automated solution to detect and align structures in brain MRI images. These two distinct approaches are intended to be compared, highlighting their respective strengths and weaknesses in the context of brain MRI slice planning.