# ConvNet-Based Localization of Anatomical Structures in 3D Medical Images

arXiv: https://arxiv.org/abs/1704.05629 · HTML: https://mlsanity.com/paper/1704.05629/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2017.

## Built on by (most cited first, 9 of 9)

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- [AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics](https://mlsanity.com/paper/2110.10332/) (2021)
- [A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning](https://mlsanity.com/paper/2203.00624/) (2022)
- [Leveraging Global Binary Masks for Structure Segmentation in Medical Images](https://mlsanity.com/paper/2205.09107/) (2022)
- [Preserving Cardiac Integrity: A Topology-Infused Approach to Whole Heart Segmentation](https://mlsanity.com/paper/2410.10551/) (2024)
- [BARL: Bilateral Alignment in Representation and Label Spaces for Semi-Supervised Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2510.16863/) (2025)
- [Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning](https://mlsanity.com/paper/2112.03276/) (2021)
- [View Classification and Object Detection in Cardiac Ultrasound to Localize Valves via Deep Learning](https://mlsanity.com/paper/2311.00068/) (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)

Localization of anatomical structures is a prerequisite for many tasks in medical image analysis. We propose a method for automatic localization of one or more anatomical structures in 3D medical images through detection of their presence in 2D image slices using a convolutional neural network (ConvNet). A single ConvNet is trained to detect presence of the anatomical structure of interest in axial, coronal, and sagittal slices extracted from a 3D image. To allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling is applied. After detection, 3D bounding boxes are created by combining the output of the ConvNet in all slices. In the experiments 200 chest CT, 100 cardiac CT angiography (CTA), and 100 abdomen CT scans were used. The heart, ascending aorta, aortic arch, and descending aorta were localized in chest CT scans, the left cardiac ventricle in cardiac CTA scans, and the liver in abdomen CT scans. Localization was evaluated using the distances between automatically and manually defined reference bounding box centroids and walls. The best results were achieved in localization of structures with clearly defined boundaries (e.g. aortic arch) and the worst when the structure boundary was not clearly visible (e.g. liver). The method was more robust and accurate in localization multiple structures.