Optimizing patient positioning during CT and MRI examinations is crucial to enhance imaging efficiency and reduce radiation exposure. This study explores the accuracy of bounding box prediction for internal body structures gained from statistical atlases based on four key points in comparison to a regression model. Therefore, we utilized a dataset including 10 828 whole-body MR volumes and extracted corresponding segmentation masks. We evaluated how effectively these boxes encompass the target structures, considering also the size. Our method yields results comparable to the regression model. However, it offers the advantage of rapid adaptability to diverse research questions, making it more flexible for various analytical scenarios.