Software:ITK-SNAP

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Short description: Medical imaging software
ITK-SNAP
Itksnaplogo.png
Itksnapsshot.png
ITK-SNAP
Developer(s)Paul A. Yushkevich, Guido Gerig, and the researchers at UPenn, UNC, Utah, and Kitware
Initial release2004
Written inC++
PlatformCross-platform
Available inEnglish
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), cone-beam computed tomography (CBCT) 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

  • Craniofacial pathologies and anatomical studies[1]
    • KCOT[2]
    • Ameloblastoma[3]
    • Cysts
    • Condyle Volumes
  • Carotid artery segmentation[4]
  • Target definition for cancer radiotherapy
    • lung cancer radiotherapy[6]

References

  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 (1): 20180113. doi:10.1259/dmfr.20180113. PMID 30216090. Bibcode: 2007SPIE.6512E..3FS. 
  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 Surgery 46 (2): 257–263. doi:10.1016/j.jcms.2017.11.012. PMID 29233700. 
  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. PMID 29496045. 
  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. doi:10.1117/12.709954. Bibcode: 2007SPIE.6512E.120S. http://link.aip.org/link/?PSISDG/6512/65123F/1. Retrieved 2007-11-08. 
  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. doi:10.1016/j.media.2006.07.003. ISSN 1361-8415. PMID 16926104. 
  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. doi:10.1118/1.2240194. Bibcode: 2006MedPh..33.1992X. http://link.aip.org/link/?MPHYA6/33/1992/4. 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 diagnosis 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". 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. PMID 16679201. 
  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. PMID 17169351. 
  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. PMID 17599704. 
  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. PMID 16513371. 
  13. ↑ Kauke, Martin; Safi, Ali-Farid; Stavrinou, Pantelis; Krischek, Boris; Goldbrunner, Roland; Timmer, Marco (2019). "Does Meningioma Volume Correlate with Clinical Disease Manifestation Irrespective of Histopathologic Tumor Grade?". Journal of Craniofacial Surgery 30 (8): e799–e802. doi:10.1097/SCS.0000000000005845. PMID 31633669. https://journals.lww.com/jcraniofacialsurgery/Fulltext/2019/12000/Does_Meningioma_Volume_Correlate_With_Clinical.144.aspx. 

External links



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