User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

By Paul Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, Guido Gerig. 2006

8,836 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “The region in which the abnormality was to be introduced was painted in using ITK-SNAP (Yushkevich et al., 2006).”
A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion (2015, positive): “Initially, the left ventricular cavity, the myocardium and the right ventricular cavity were manually segmented in 20 subjects at the ED and ES phase using the software itkSNAP (Yushkevich et al., 2006) by experienced clinicians in accordance with the SCMR recommendations (Schulz-Menger et al.”
A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence (2021, methodology): “Data analysis and visualization were performed using custom code in MATLAB and Python as well as tools from various packages, such as FreeSurfer, SPM, FSL, ANTs 74 and ITK-SNAP 75 .”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, background): “They are typically obtained by the meticulous and costly effort of a domainspecific expert who relies on an interactive visualization software, such as (Criminisi et al., 2008; Heiberg et al., 2010; Pieper et al., 2004; Yushkevich et al., 2006), and might exploit multiple imaging modalities, while r”
Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map (2019, methodology): “The annotations were performed on ITK-SNAP [43] where samples could be enlarged so that each image pixel occupied several pixels on the monitor.”
Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images (2016, methodology): “The manual segmentation was performed slice-by-slice, using the brush tool in the ITK-SNAP [33], and the gold standard segmentation was achieved by averaging the three manual delineations using the shape-based approach [34].”
Confirmation of functional zones within the human subthalamic nucleus: Patterns of connectivity and sub-parcellation using diffusion weighted imaging (2012, methodology): “…= InNMF = Non-negative matrix factorisation, RN = Red nucleus, SN = Substantia nigra, were then subdivided using ITK-SNAP (http://www.itksnap.org) (Yushkevich et al., 2006) “draw over label” function, into the internal (GPi) and external globus pallidus (GPe) using the medial medullary lamina as…”
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation (2020, methodology): “3 shows 3 cases in 2D and 3D with ITK-SNAP [66].”
Large-scale pancreatic cancer detection via non-contrast CT and deep learning (2023, neutral): “Our model can detect the presence or absence of a pancreatic lesion, segment the lesion, and classify the lesion subtypes (Fig.”
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets (2009, background): “Main objectives of the workshop were to evaluate how far automatic segmentation methods have come in recent years and to provide a state-of-the-art overview of current techniques.”
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration (2018, methodology): “generated on the reconstructed images using Auto-Net [39] and manually corrected in ITK-SNAP [40] as needed.”
Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation (2015, methodology): “ITK-SNAP (Yushkevich et al., 2006) (www.itksnap.org) was particularly used for interactive manual editing.”