User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP
Open accessAbstract
ITK-SNAP is an interactive software tool for manual and semi-automatic segmentation of 3D medical images. This paper summarizes major new features added to ITK-SNAP over the last decade. The main focus of the paper is on new features that support semi-automatic segmentation of multi-modality imaging datasets, such as MRI scans acquired using different contrast mechanisms (e.g., T1, T2, FLAIR). The new functionality uses decision forest classifiers trained interactively by the user to transform multiple input image volumes into a foreground/background probability map; this map is then input as the data term to the active contour evolution algorithm, which yields regularized surface representations of the segmented objects of interest. The new functionality is evaluated in the context of high-grade and low-grade glioma segmentation by three expert neuroradiogists and a non-expert on a reference dataset from the MICCAI 2013 Multi-Modal Brain Tumor Segmentation Challenge (BRATS). The accuracy of semi-automatic segmentation is competitive with the top specialized brain tumor segmentation methods evaluated in the BRATS challenge, with most results obtained in ITK-SNAP being more accurate, relative to the BRATS reference manual segmentation, than the second-best performer in the BRATS challenge; and all results being more accurate than the fourth-best performer. Segmentation time is reduced over manual segmentation by 2.5 and 5 times, depending on the rater. Additional experiments in interactive placenta segmentation in 3D fetal ultrasound illustrate the generalizability of the new functionality to a different problem domain.
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Automated methods of segmenting first-trimester placentas (n = 13) in an anterior position using joint label fusion and majority vote achieved a mean DSC of 0.83 ± 0.05.
“Using 3D-US, automated methods of segmenting first trimester placentas (n = 13) in an anterior position using a joint label fusion and majority vote technique achieved a mean DSC of 0.83 ± 0.05 [20], [21].”
From Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment (2021) · cites this paper as [21]
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- Authors
- Yushkevich, P. A., Pashchinskiy, A., Oguz, I., Mohan, S., Schmitt, J. E., Stein, J. M., Zukić, D., Vicory, J., McCormick, M., Yushkevich, N., Schwartz, N., Gao, Y., & Gerig, G.
- Affiliations
- University of Pennsylvania (US)
- Kitware (United States) (US)
- University of Utah (US)
- New York University (US)
- Corresponding author
- Yushkevich, Paul A.
- ORCID
- Venue
- Neuroinformatics
- Published
- 2018-06-27
- Volume
- 17
- Issue
- 1
- Pages
- 83-102
- ISSN
- 1539-2791 (ISSN-L)
- 1559-0089
- PMC
- PMC6310114
- OpenAlex
- W2810700317
- Cited by
- 175
- References
- 36
- Citations by year
- 2026: 19 · 2025: 30 · 2024: 32 · 2023: 19 · 2022: 28 · 2021: 28 · 2020: 12 · 2019: 6
- FWCI
- 6.00
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Neuroinformatics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01 EB014346, R01 EB017255)
- National Institute of Environmental Health Sciences (K01 ES026840)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (U01 HD087180)
- 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 › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- ITK-SNAP, semi-automatic segmentation, MRI segmentation, brain tumor segmentation, glioma segmentation, BraTS, placenta segmentation, 3D fetal ultrasound
- MeSH terms
- Algorithms, Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Software, Imaging, Three-Dimensional, Neuroimaging
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