Medical Image Processing, Analysis and Visualization in clinical research

By M. McAuliffe, Francois M. Lalonde, Delia P. McGarry, W. Gandler, K. Csaky, B. Trus. Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001, 2001

583 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/67b5929fb812f31c68846c37347b17726283b8a5

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Citations in the corpus

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability (2006, methodology): “, 2001) include 3D active contour segmentation modules, and the NIH MIPAV tool (McAuliffe et al., 2001) provides in-slice active contour segmentation.”
Anatomically motivated modeling of cortical laminae (2014, methodology): “The method described in this paper to compute intracortical stratifications with the equidistant and the equi-volume model has been made publicly available as a plug-in for the MIPAV software package (McAuliffe et al., 2001) and the JIST pipeline environment (Lucas et al., 2010).”
Longitudinal multiple sclerosis lesion segmentation: Resource and challenge (2017, unknown): “Beyond this there is a desire for measures that help in identifying the pathophysiologic stages of MS lesions (pre-active, active, chronic active, or chronic inactive) (Jonkman et al., 2015). also a good idea to familiarize yourself with the paint and mask functions in MIPAV (McAuliffe et al., 2001;”
Visualization of image data from cells to organisms (2010, unknown): “Extensible tool suites such as ITK, SCIRun, MIPAV and ImageJ provide collections of automated approaches to the general problem of segmentation for two-and 3D images, as well as tools designed to extract specific anatomical structures.”
3D Slicer as an Image Computing Platform for the Quantitative Imaging Network (2012, methodology): “Several other tools, such as OsiriX [26], BioImage Suite, MIPAV [27] and ImageJ [28], are similar to Slicer in that they provide extensible development platforms for biomedical imaging applications (for a comprehensive comparison of these tools we refer the reader to the earlier surveys [29,30]).”
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python (2011, methodology): “, 2001), MIPAV (McAuliffe et al., 2001), SPM include their own batch processing tools, but do not allow mixing components from other packages.”
Automatic and quantitative measurement of protein-protein colocalization in live cells. (2004, methodology): “Our algorithm has been commercialized by Bitplane AG (Zurich, Switzerland) and made available to the intramural National Institutes of Health research community as part of the software MIPAV (McAuliffe et al., 2001).”
Multi-Parametric Neuroimaging Reproducibility: A 3T Resource Study (2010, methodology): “Dr. Peter C.M. van Zijl is a paid lecturer for Philips Healthcare and has technology licensed to them.”
A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions (2009, unknown): “We have released the algorithm as a user-friendly plug-in to MIPAV (McAuliffe et al., 2001) which is a freely available software developed at the National Institutes of Health for the analysis and visualization of medical images.”
Using plant microfossils from dental calculus to recover human diet: a case study from Tell al-Raqā'i, Syria (2008, methodology): “The before and after composite images were then compared using MIPAV, a software package designed by the NIH for processing medical images ( McAuliffe et al., 2001 ).”
DRAMMS: Deformable Registration via Attribute Matching and Mutual-Saliency Weighting (2009, methodology): “Note that, the default set of parameters is used for MI-based FFD as provided in MIPAV, a public software package [32].”
PANDA: a pipeline toolbox for analyzing brain diffusion images (2013, methodology): “Currently, there have been a few packages such as MIPAV (McAuliffe et al., 2001), JIST (Lucas et al., 2010), Nipype (Gorgolewski et al., 2011), and LONI (Dinov et al., 2009), which aim to facilitate automated processing of neuroimaging dataset.”