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ITK-SNAP

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This is an old revision of this page, as edited by Yushkevich (talk | contribs) at 03:45, 8 November 2007 (→‎History). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

ITK-SNAP is an open-source software application for medical image segmentation. It's primary use is for delineating anatomical structures and regions in MRI, CT, Ultrasound and other three-dimensional medical images [1][2][3] [4] [5] [6].

ITK-SNAP can be used in two modes. The first mode is manual delineation, where the user traces the boundaries of structures in three orthogonal slice planes through the medical image. The second mode is automatic segmentation, which uses the level set method. The user places one or more seeds in the image, and seeds grow constrained by intensity edges in the image. In neuroimaging, ITK-SNAP is used to segment the hippocampus (in manual mode), ventricles (in automatic mode), caudate nucleus (automatic mode with manual touchup), and many other structures of interest.

History

ITK-SNAP was developed by image analysis researchers at the University of Pennsylvania and University of North Carolina at Chapel Hill[7]. Development began at UNC-CH as a series of graduate student projects in the Computer Science Department. Subsequent development was funded by the National Library of Medicine and performed at the PICSL group [1] at the Department of Radiology at the University of Pennsylvania.

References

  1. ^ Spangler, E.L. (2007). "Evaluation of internal carotid artery segmentation by InsightSNAP". Proceedings of SPIE. 6512: 65123F. Retrieved 2007-11-08. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  2. ^ 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. Retrieved 2007-11-08. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  3. ^ 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. Retrieved 2007-11-08. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  4. ^ 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)
  5. ^ 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. Retrieved 2007-11-08. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  6. ^ Krishnan, S. (2006). "Accuracy of spatial normalization of the hippocampus: implications for fMRI research in memory disorders". Neuroimage. 31 (2): 560–571. Retrieved 2007-11-08. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  7. ^ Yushkevich, P.A. (2006). "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability". Neuroimage. 31 (3): 1116–28. Retrieved 2007-11-08. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
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