Medical Image Segmentation: Methods and Software
Abstract
Methods for segmentation of medical images are divided into three generations, where each generation adds an additional level of algorithmic complexity. The first generation is composed of the simplest forms of image analysis such as the use of intensity thresholds and region growing. The second generation is characterized by the application of uncertainty models and optimization methods, and the third generation incorporates knowledge into the segmentation process. Sources of segmentation software from industry and academia are identified along with databases for segmentation validation.
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These deep CNN interactive or semi-automatic methods are not fully interactive for testing since they do not accept further interactions for refinement.
“However, these methods are not fully interactive for testing since they do not accept further interactions for refinement.”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [21]
3D U-Net learns from sparsely annotated images and can be used for semi-automatic segmentation.
“For instance, 3D U-Net [21] learns from sparsely annotated images and can be used for semi-automatic segmentation.”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [21]
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Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., & Vercauteren, T. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.103 · Cited by 659 · see citing works
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
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“During the past decades, researchers have developed many automatic segmentation approaches [1].”
BackgroundBai, J., Sun, Z., Yu, S., Lu, Y., Long, S., Wang, H., Qiu, R., Ou, Z., Zhou, M., Zhi, D., et al. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.940150 · Cited by 36 · see citing works
- Authors
- Withey, D. J., & Koles, Z. J.
- Affiliations
- University of Alberta (CA)
- Venue
- 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging
- Publisher
- IEEE
- Published
- 2007-10
- Pages
- 140-143
- OpenAlex
- W2130748174
- Cited by
- 113
- References
- 39
- Citations by year
- 2025: 4 · 2024: 1 · 2023: 5 · 2022: 9 · 2021: 7 · 2020: 4 · 2019: 8 · 2018: 9 · 2017: 4 · 2016: 7 · 2015: 8 · 2014: 11 · 2013: 15 · 2012: 7
- FWCI
- 2.19
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Joint Meeting of International Symposium on Noninvasive Functional Source Imaging of Brain and Heart and International Conference on Functional Biomedical Imaging (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- medical image segmentation, intensity thresholding, region growing, uncertainty models, optimization methods, knowledge-based segmentation
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