GIST: an interactive, GPU-based level set segmentation tool for 3D medical images
Open accessAbstract
While level sets have demonstrated a great potential for 3D medical image segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications. The second problem is compounded by the first. This paper describes a new tool for 3D segmentation that addresses these problems by computing level-set surface models at interactive rates. This tool employs two important, novel technologies. First is the mapping of a 3D level-set solver onto a commodity graphics card (GPU). This mapping relies on a novel mechanism for GPU memory management. The interactive rates level-set PDE solver give the user immediate feedback on the parameter settings, and thus users can tune free parameters and control the shape of the model in real time. The second technology is the use of intensity-based speed functions, which allow a user to quickly and intuitively specify the behavior of the deformable model. We have found that the combination of these interactive tools enables users to produce good, reliable segmentations. To support this observation, this paper presents qualitative results from several different datasets as well as a quantitative evaluation from a study of brain tumor segmentations.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 59 · Cited by 102
Research works printed but not linked: 4
- Lorigo, L., Faugeras, O., Grimson, E., Keriven, R., Kikinis, R., Nabavi, A., Westin, C.F.: Co-dimension 2 geodesic active contours for the segmentation of tubular strucures. In: Proceedings of IEEE Conf. on Comp. Vision and Pattern Recognition. (2000) 444-452
- Osher, S., Sethian, J.: Fronts propogating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 79 (1988) 12-49
- Owens, J.D.: Computer Graphics on a Stream Architecture. PhD thesis, Stanford University (2002)
- Lefohn, A., Cates, J., Whitaker, R.: Interactive, gpu-based level sets for 3d brain tumor segmentation. In: MICCAI 2003. (2003) To appear
- Authors
- CATES, J., LEFOHN, A., & WHITAKER, R.
- Affiliations
- University of Utah (US)
- Corresponding author
- CATES, J
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2004-09
- Volume
- 8
- Issue
- 3
- Pages
- 217-231
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2140041787
- Cited by
- 102
- References
- 59
- Citations by year
- 2026: 1 · 2024: 1 · 2022: 2 · 2021: 4 · 2020: 4 · 2019: 9 · 2018: 6 · 2017: 6 · 2016: 3 · 2015: 7 · 2014: 6 · 2013: 7 · 2012: 6
- FWCI
- 5.14
- Type
- journal article
- Language
- en
- Open access
- green
- www.escholarship.org
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- level set segmentation, GPU computing, 3D medical image segmentation, interactive segmentation, GPU memory management, deformable models, brain tumor segmentation, parameter tuning, partial differential equations
- MeSH terms
- Algorithms, Brain Neoplasms, Computer Graphics, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Software, User-Computer Interface, Reproducibility of Results, Imaging, Three-Dimensional
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
