ConvNet-Based Localization of Anatomical Structures in 3-D Medical Images
Open accessAbstract
Localization of anatomical structures is a prerequisite for many tasks in a medical image analysis. We propose a method for automatic localization of one or more anatomical structures in 3-D medical images through detection of their presence in 2-D image slices using a convolutional neural network (ConvNet). A single ConvNet is trained to detect the presence of the anatomical structure of interest in axial, coronal, and sagittal slices extracted from a 3-D image. To allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling is applied. After detection, 3-D 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 the 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.
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de Vos et al. (2017) proposed a novel approach which can localize anatomical structures in 3D medical images.
“de Vos et al. (2017) proposed a novel approach which can localize anatomical structures in 3D medical images.”
From Self-supervised learning for medical image analysis using image context restoration (2019) · cites this paper as de Vos et al., 2017
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- Authors
- de Vos, B. D., Wolterink, J. M., de Jong, P. A., Leiner, T., Viergever, M. A., & Isgum, I.
- Affiliations
- University Medical Center Utrecht (NL)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2017-07
- Volume
- 36
- Issue
- 7
- Pages
- 1470-1481
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2590651460
- Cited by
- 133
- References
- 31
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 15 · 2023: 8 · 2022: 16 · 2021: 18 · 2020: 16 · 2019: 25 · 2018: 18 · 2017: 3
- FWCI
- 7.04
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Netherlands Organization for Scientific Research Foundation for Technology Sciences Project 12726
- NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- anatomical structure localization, convolutional neural networks, spatial pyramid pooling, chest CT, cardiac CT angiography, abdominal CT, liver localization
- MeSH terms
- Angiography, Humans, Liver, Tomography, X-Ray Computed, Neural Networks, Computer, Imaging, Three-Dimensional
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