  This paper discusses current methods and trends for 3D bounding box detection
in volumetric medical image data. For this purpose, an overview of relevant
papers from recent years is given. 2D and 3D implementations are discussed and
compared. Multiple identified approaches for localizing anatomical structures
are presented. The results show that most research recently focuses on Deep
Learning methods, such as Convolutional Neural Networks vs. methods with manual
feature engineering, e.g. Random-Regression-Forests. An overview of bounding
box detection options is presented and helps researchers to select the most
promising approach for their target objects.
