Work

User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP

Year: 2018

Type: article

Source: Neuroinformatics

DOI: 10.1007/s12021-018-9385-x

Authors: Paul A. Yushkevich, Artem Pashchinskiy, Ipek Oguz, Suyash Mohan, J. Eric Schmitt, Joel M. Stein, Dženan Zukić, Jared Vicory, Matthew McCormick, Natalie Yushkevich, Nadav Schwartz, Yang Gao, Guido Gerig

Institutions: University of Pennsylvania, Kitware (United States), University of Utah, New York University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Environmental Health Sciences, National Institute of Biomedical Imaging and Bioengineering, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Awards: Continued Development and Maintenance of ITK-SNAP 3D Image Segmentation Software, Developing a multi-modality, paradigm-shifting approach for in vivo assessment of the human placenta and the impact of maternal nutrition on its development and function, Adaptive Large-Scale Framework for Automatic Biomedical Image Segmentation, INGOT: a family of statistical computing algorithms for hypothesis-driven imaging genomic and longitudinal neuroimaging analysis

OpenAlex ID: W2810700317

FWCI: 5.667

Cites: 36

Cited by: 188

Related to: 10