Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig GUser-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116-1128

Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina, United States
NeuroImage (Impact Factor: 6.36). 08/2006; 31(3):1116-28. DOI: 10.1016/j.neuroimage.2006.01.015
Source: PubMed


Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle segmentation are provided.

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    • "Pixel size of these images ranged from 0.10 to 0.21 mm, slice thickness was 0.5 mm for all individuals, and slice interval ranged from 0.20 to 0.50 mm. Virtual endocasts were reconstructed from these CT images using a threshold-based 2D segmentation procedure in ITK-SNAP 3.0 (Yushkevich et al., 2006; Aristide et al., 2015). A threshold gray value was defined to separate bone from empty spaces. "
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    • "InsightToolkit (ITK, RRID:nif-0000-00319, Schroeder 2005, compiled with Python wrappings (WrapITK, Lehmann et al. 2006), Convert3d (Yushkevich et al. 2006b,, RRID:nif-0000-00317) and ImageMagick ( "
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    ABSTRACT: Techniques based on imaging serial sections of brain tissue provide insight into brain structure and function. However, to compare or combine them with results from three dimensional imaging methods, reconstruction into a volumetric form is required. Currently, there are no tools for performing such a task in a streamlined way. Here we propose the Possum volumetric reconstruction framework which provides a selection of 2D to 3D image reconstruction routines allowing one to build workflows tailored to one's specific requirements. The main components include routines for reconstruction with or without using external reference and solutions for typical issues encountered during the reconstruction process, such as propagation of the registration errors due to distorted sections. We validate the implementation using synthetic datasets and actual experimental imaging data derived from publicly available resources. We also evaluate efficiency of a subset of the algorithms implemented. The Possum framework is distributed under MIT license and it provides researchers with a possibility of building reconstruction workflows from existing components, without the need for low-level implementation. As a consequence, it also facilitates sharing and data exchange between researchers and laboratories.
    Full-text · Article · Dec 2015 · Neuroinformatics
    • "Manual segmentation was performed using ITK-SNAP software (v.2.4.0,, last accessed November 27, 2015; Yushkevich et al. 2006) and was doublechecked by 2 different investigators. "
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