ITK-SNAP

ITK-SNAP

ITK-SNAP
Developer(s) Researchers at UPenn and UNC
Initial release 2004
Written in C++
Platform Cross-platform
Available in English language
Type Health Software
License GNU General Public License
Website http://www.itksnap.org

ITK-SNAP is an interactive software application that allows users to navigate three-dimensional medical images, manually delineate anatomical regions of interest, and perform automatic image segmentation. The software was designed with the audience of clinical and basic science researchers in mind, and emphasis has been placed on having a user-friendly interface and maintaining a limited feature set to prevent feature creep. ITK-SNAP is most frequently used to work with magnetic resonance imaging (MRI) and computed tomography (CT) data sets.

Features

The purpose of the tool is to make it easy for researchers to delineate anatomical structures and regions of interest in imaging data. The set of features is kept to a minimum. The main features of the program are

Image navigation
three orthogonal cut planes through the image volume are shown at all times. The cut planes are linked by a common cursor, so that moving the cursor in one cut plane updates the other cut planes. The cursor is moved by dragging the mouse over the cut planes, making for smooth navigation. The linked cursor also works across ITK-SNAP sessions, making it possible to navigate multimodality imaging data (e.g., two MRI scans of a subject from a single session).
Manual segmentation 
ITK-SNAP provides tools for manual delineation of anatomical structures in images. Labeling can take place in all three orthogonal cut planes and results can be visualized as a three-dimensional rendering. This makes it easier to ensure that the segmentation maintains reasonable shape in 3D.
Automatic segmentation
ITK-SNAP provides automatic functionality segmentation using the level set method. This makes it possible to segment structures that appear somewhat homogeneous in medical images using very little human interaction. For example, the lateral ventricles in MRI can be segmented reliably, as can some types of tumors in CT and MRI.

ITK-SNAP is open source software distributed under the GNU General Public License. It is written in C++ and it leverages the Insight Segmentation and Registration Toolkit (ITK) library. ITK-SNAP can read and write a variety of medical image formats, including DICOM, NIfTI, and Mayo Analyze. It also offers limited support for multi-component (e.g., diffusion tensor imaging) and multi-variate imaging data.

Applications

ITK-SNAP has been applied in the following areas

References

  1. ↑ Spangler, E.L.; Brown, C.; Roberts, J.A.; Chapman, B.E. (2007). "Evaluation of internal carotid artery segmentation by InsightSNAP". Proceedings of SPIE. 6512 (5): 65123F. Bibcode:2007SPIE.6512E.120S. doi:10.1117/12.709954. Retrieved 2007-11-08.
  2. ↑ Corouge, I.; Fletcher, T.; Joshi, S.; Gouttard, S.; Gerig, G. (Oct 2006). "Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis". Medical image analysis. 10 (5): 786–798. doi:10.1016/j.media.2006.07.003. ISSN 1361-8415. PMID 16926104.
  3. ↑ Xiao, Y.; Werner-wasik, M.; Curran, W.; Galvin, J. (2006). "SU-EE-A2-03: Evaluation of Auto-Segmentation Tools for the Target Definition for the Treatment of Lung Cancer". Medical Physics. 33 (6): 1992. Bibcode:2006MedPh..33.1992X. doi:10.1118/1.2240194. Retrieved 2007-11-08.
  4. ↑ D'addario, V.; Pinto, V.; Pintucci, A.; Di Cagno, L. (2007). "OP13. 04: Accuracy of six sonographic signs in the prenatal dignosis of spina bifida". Ultrasound in Obstetrics and Gynecology. 30 (4): 498–498. doi:10.1002/uog.4534. Retrieved 2007-11-08.
  5. ↑ Rizzi, S.H.; Banerjee, P.P.; Luciano, C.J. (2007). "Automating the Extraction of 3D Models from Medical Images for Virtual Reality and Haptic Simulations". Automation Science and Engineering, 2007. CASE 2007. IEEE International Conference on. pp. 152–157.
  6. ↑ Cavidanes, L.H.; Styner, M.; Proffit, W.R. (2006). "Image analysis and superimposition of 3-dimensional cone beam computed tomography models". American Journal of Orthodontics and Dentofacial Orthopedics. 129 (5): 611–618. doi:10.1016/j.ajodo.2005.12.008. PMC 3586191Freely accessible. PMID 16679201. Retrieved 2007-11-08.
  7. ↑ MacHado, A.M.C.; Simon, T.J.; Nguyen, V.; McDonald-mcginn, D.M.; Zackai, E.H.; Gee, J.C. (2007). "Corpus callosum morphology and ventricular size in chromosome 22q11.2 deletion syndrome". Brain Research. 1131 (1): 197–210. doi:10.1016/j.brainres.2006.10.082. PMC 1802103Freely accessible. PMID 17169351.
  8. ↑ Apostolova, L.G.; Thompson, P.M. (2007). "Brain mapping as a tool to study neurodegeneration". Neurotherapeutics. 4 (3): 387–400. doi:10.1016/j.nurt.2007.05.009. PMC 2634605Freely accessible. PMID 17599704.
  9. ↑ Krishnan, S.; Slavin, M.J.; Tran, T.T.; Doraiswamy, P.M.; Petrella, J.R. (2006). "Accuracy of spatial normalization of the hippocampus: implications for fMRI research in memory disorders". NeuroImage. 31 (2): 560–571. doi:10.1016/j.neuroimage.2005.12.061. PMID 16513371. Retrieved 2007-11-08.

External links

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