论文标题
体积医学图像数据中的3D边界框检测：系统文献综述
3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review
论文作者
论文摘要
本文讨论了体积医学图像数据中3D边界框检测的当前方法和趋势。为此，给出了近年来相关论文的概述。讨论和比较2D和3D实现。提出了多种鉴定出的定位解剖结构的方法。结果表明，大多数研究最近侧重于深度学习方法，例如卷积神经网络与手动功能工程的方法，例如随机回归孔。提出了边界框检测选项的概述，并帮助研究人员为目标对象选择最有前途的方法。
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.
