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.
@article{arxiv.2012.05745,
title = {3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review},
author = {Daria Kern and Andre Mastmeyer},
journal= {arXiv preprint arXiv:2012.05745},
year = {2021}
}