Random Walks for Interactive Organ Segmentation in Two and Three Dimensions: Implementation and Validation
Abstract
A new approach to interactive segmentation based on random walks was recently introduced that shows promise for allowing physicians more flexibility to segment arbitrary objects in an image. This report has two goals: To introduce a novel computational method for applying the random walker algorithm in 2D/3D using the Graphics Processing Unit (GPU) and to provide quantitative validation studies of this algorithm relative to different targets, imaging modalities and interaction strategies.
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Because scribbles are placed only in mis-segmented areas, their position and length vary less than freely drawn scribbles in methods such as Random Walks and Slic-Seg, and BIFSeg output varies only slightly with different scribbles.
“Since the scribbles are provided only in mis-segmented areas, the variations of position and length of scribbles are limited and much smaller than that of freely drawn scribbles used in traditional methods such as Random Walks [18] and Slic-Seg [21], and the output of BIFSeg also varies slightly with varying scribbles, as shown in Fig. 8.”
From Interactive medical image segmentation using deep learning with image-specific fine tuning (2018) · cites this paper as [18]
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Random Walks for Image Segmentation
Grady, L. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.233 · Cited by 2,624 · see citing works
Self-citationInteractive medical image segmentation using deep learning with image-specific fine tuning
Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2791721 · Cited by 843 · see citing works
Wang, G., Zuluaga, M. A., Pratt, R., Aertsen, M., Doel, T., Klusmann, M., David, A. L., Deprest, J., Vercauteren, T., & Ourselin, S. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.04.009 · Cited by 73 · see citing works
Dynamically Balanced Online Random Forests for Interactive Scribble-Based Segmentation
Wang, G., Zuluaga, M. A., Pratt, R., Aertsen, M., Doel, T., Klusmann, M., David, A. L., Deprest, J., Vercauteren, T., & Ourselin, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_41 · Cited by 14 · see citing works
- Authors
- Grady, L., Schiwietz, T., Aharon, S., & Westermann, R.
- Affiliations
- Siemens (United States) (US)
- Technical University of Munich (DE)
- ORCID
- Westermann, Rüdiger
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2005
- Volume
- 8
- Issue
- Pt 2
- Pages
- 773-780
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1590367972
- Cited by
- 113
- References
- 0
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 2 · 2022: 5 · 2021: 8 · 2020: 2 · 2019: 8 · 2018: 7 · 2017: 4 · 2016: 7 · 2015: 10 · 2014: 8 · 2013: 3 · 2012: 9
- FWCI
- 4.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed (publishedVersion)
- 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
- random walker algorithm, interactive segmentation, organ segmentation, GPU implementation, 3D medical image segmentation, segmentation validation, medical imaging modalities
- MeSH terms
- Algorithms, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Models, Biological, Pattern Recognition, Automated, Sensitivity and Specificity, Signal Processing, Computer-Assisted, User-Computer Interface, Viscera, Reproducibility of Results, Imaging, Three-Dimensional
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