3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review
This review helps researchers in medical imaging select promising approaches for localizing anatomical structures using 3D bounding box detection.
This systematic literature review analyzes recent methods for 3D bounding box detection in volumetric medical image data, comparing 2D and 3D implementations. It finds that most recent research focuses on Deep Learning methods like Convolutional Neural Networks over manual feature engineering approaches.
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.