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

arXiv: https://arxiv.org/abs/2012.05745 · HTML: https://mlsanity.com/paper/2012.05745/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Comparison of 2D vs. 3D U-Net Organ Segmentation in abdominal 3D CT images](https://mlsanity.com/paper/2107.04062/) (2021)
- [Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus](https://mlsanity.com/paper/2303.17915/) (2023)
- [A tomographic workflow to enable deep learning for X-ray based foreign object detection](https://mlsanity.com/paper/2201.12184/) (2022)
- [Gravity Network for end-to-end small lesion detection](https://mlsanity.com/paper/2309.12876/) (2023)
- [YOLO-PVC: 2D-to-3D Consolidation of Slice-wise Detections for Volumetric Liver Tumor Localization in MRI](https://mlsanity.com/paper/2608.04642/) (2026)

## Abstract (arXiv)

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.