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ITK-SNAP

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ITK-SNAP
Developer(s)Researchers at UPenn and UNC
Initial release2004
Written inC++
PlatformCross-platform
Available inEnglish language
TypeHealth Software
LicenseGNU General Public License
Websitehttp://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.

History

Development began in 1999 as a series of software engineering team projects by graduate students at the Computer Science department at the University of North Carolina at Chapel Hill. The projects were conceived and supervised by Professor Guido Gerig. The first of these projects developed an image navigation tool (IRIS). Later, an automatic active contour segmentation module (SnAP) was added. In 2003, the National Library of Medicine funded a project to integrate IRIS/SnAP with its Insight Segmentation and Registration Toolkit - and ITK-SNAP was born. Since 2003, development has been led by Paul Yushkevich at the Penn Image Computing and Science Laboratory at the University of Pennsylvania. [1].

Notable 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. ^ Yushkevich, P.A. (2006). "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability". Neuroimage. 31 (3): 1116–28. doi:10.1016/j.neuroimage.2006.01.015. Retrieved 2007-11-08. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  2. ^ Spangler, E.L. (2007). "Evaluation of internal carotid artery segmentation by InsightSNAP". Proceedings of SPIE. 6512: 65123F. doi:10.1117/12.709954. Retrieved 2007-11-08. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  3. ^ Attention: This template ({{cite pmid}}) is deprecated. To cite the publication identified by PMID 16926104, please use {{cite journal}} with |pmid=16926104 instead.
  4. ^ Xiao, Y. (2006). "SU-EE-A2-03: Evaluation of Auto-Segmentation Tools for the Target Definition for the Treatment of Lung Cancer". Medical Physics. 33: 1992. doi:10.1118/1.2240194. Retrieved 2007-11-08. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  5. ^ D'addario, V. (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. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  6. ^ Rizzi, S.H. (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. {{cite conference}}: Cite has empty unknown parameter: |conferenceurl= (help); Unknown parameter |booktitle= ignored (|book-title= suggested) (help); Unknown parameter |coauthors= ignored (|author= suggested) (help)
  7. ^ Cavidanes, L.H. (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. Retrieved 2007-11-08. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  8. ^ Apostolova, L.G.; Thompson, P.M. (2007), "Brain mapping as a tool to study neurodegeneration", Neurotherapeutics, 4 (3): 387–400, retrieved 2009-05-20
  9. ^ Krishnan, S. (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. Retrieved 2007-11-08. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
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