3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic
Literature Review
Abstract
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.