TY - JOUR
T1 - User-guided 3D active contour segmentation of anatomical structures
T2 - Significantly improved efficiency and reliability
AU - Yushkevich, Paul A.
AU - Piven, Joseph
AU - Hazlett, Heather Cody
AU - Smith, Rachel Gimpel
AU - Ho, Sean
AU - Gee, James C.
AU - Gerig, Guido
N1 - Funding Information:
The integration of the SNAP tool with ITK was performed by Cognitica Corporation under NIH/NLM PO 467-MZ-202446-1. The validation study is supported by the NIH/NIBIB P01 EB002779, NIH Conte Center MH064065, and UNC Neurodevelopmental Disorders Research Center, Developmental Neuroimaging Core. The MRI images of infants and expert manual segmentations are funded by NIH RO1 MH61696 and NIMH MH 64580 (PI: Joseph Piven). Manual segmentations for the caudate study were done by Michael Graves and Todd Mathews; SNAP caudate segmentation was performed by Rachel Smith and Michael Graves; Rachel Smith and Carolyn Kylstra were raters for the SNAP ventricle segmentation.
PY - 2006/7/1
Y1 - 2006/7/1
N2 - 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.
AB - 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.
KW - 3D active contour models
KW - Anatomical objects
KW - Caudate nucleus
KW - Computational anatomy
KW - Image segmentation
KW - Open source software
KW - Validation
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U2 - 10.1016/j.neuroimage.2006.01.015
DO - 10.1016/j.neuroimage.2006.01.015
M3 - Article
C2 - 16545965
AN - SCOPUS:33744930583
SN - 1053-8119
VL - 31
SP - 1116
EP - 1128
JO - NeuroImage
JF - NeuroImage
IS - 3
ER -