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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), cone-beam computed tomography (CBCT) and computed tomography (CT) data sets.

Itk Snap Software

The Medical Imaging Interaction Toolkit (MITK) is a free open-source software system for development of interactive medical image processing software.MITK combines the Insight Toolkit (ITK) and the Visualization Toolkit (VTK) with an application framework. Download ITK-SNAP - Import, analyze and modify 2D and 3D medial image files with various viewing and editing tools included in this practical application.

Features[edit]

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

Itk Snap For T1

1. Questions / Problems. The ITK Software Guide is a good reference when getting started with ITK. For questions or problems, join the ITK Discourse, and post your questions there.Or, send an email to: kitware@kitware.com.
2. ITK-SNAP 3.4.0-Qt4 (compatibility version) These executables have the same functionality as ITK-SNAP 3.4.0 but are compiled with the older version of the Qt toolkit, Qt 4.8.6. If you experience issues with image and 3D graphics display, these builds are likely to work better on your hardware.
3. Sorry, but Snap for Mac does not have a direct download. Use the link below and download the required product from the App Store.

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[edit]

Itk Snap 3.6

ITK-SNAP has been applied in the following areas

• Craniofacial pathologies and anatomical studies^[1]
  • KCOT^[2]
  • Ameloblastoma^[3]
• Carotid artery segmentation^[4]

• Diffusion MRI Analysis^[5]

Itk Snap Github

• Target definition for cancer radiotherapy
  • lung cancer radiotherapy^[6]

• Prenatal Image Analysis
  • Diagnosis of spina bifida^[7]
• Virtual reality in Medicine^[8]
• Orthodontics^[9]
• Brain morphometry
  • Corpus callosum and ventricle analysis in 22q11.2 deletion syndrome^[10]
  • Hippocampus size and shape measurement in neurodegenerative disorders.^[11][12]
  • Human brain tumors (e.g., Meningioma)^[13]

Itk Software

References[edit]

Itk Snap Segmentation

1. ^Kauke, Martin Kauke and Ali-Farid Safi (January 2019). 'Image segmentation-based volume approximation - volume as a factor in the clinical management of osteolytic jaw lesions'. Dentomaxillofacial Radiology. 48: 20180113. Bibcode:2007SPIE.6512E.120S. doi:10.1259/dmfr.20180113. PMC6398913. PMID30216090.
2. ^Kauke, Martin (February 2018). 'Volumetric analysis of keratocystic odontogenic tumors and non-neoplastic jaw cysts – Comparison and its clinical relevance'. Journal of Cranio-Maxillofacial Surrgery. 46 (2): 257–263. doi:10.1016/j.jcms.2017.11.012. PMID29233700.
3. ^Safi, Ali-Farid (2018). 'Does volumetric measurement serve as an imaging biomarker for tumor aggressiveness of ameloblastomas?'. Oral Oncology. 78 (March 2018): 16–24. doi:10.1016/j.oraloncology.2018.01.002. PMID29496045.
4. ^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.^{[permanent dead link]}
5. ^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. CiteSeerX10.1.1.101.8505. doi:10.1016/j.media.2006.07.003. ISSN1361-8415. PMID16926104.
6. ^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. Archived from the original on 2013-02-23. Retrieved 2007-11-08.
7. ^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. doi:10.1002/uog.4534.
8. ^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. doi:10.1109/COASE.2007.4341748.
9. ^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. PMC3586191. PMID16679201.
10. ^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. PMC1802103. PMID17169351.
11. ^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. PMC2634605. PMID17599704.
12. ^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. PMID16513371.
13. ^https://journals.lww.com/jcraniofacialsurgery/Fulltext/2019/12000/Does_Meningioma_Volume_Correlate_With_Clinical.144.aspx

External links[edit]

• Main ITK-SNAP website (downloads, bugs, mailing lists)