3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support

By Ron Kikinis, S. Pieper, Kirby G. Vosburgh. 2014

1,165 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/84c83984c91a03447d9f88b30d54b387379a4a17

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. A. Torrado-Carvajal
2. D. Izquierdo-García
3. Javier Vera-Olmos
4. M. Salvatore
5. M. Morales
6. C. Catana
7. A. Soricelli
8. J. Margolin

Citations in the corpus

• Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction (2018, methodology): “First, MRI bias was corrected using the N4ITK MRI bias correction module in 3D Slicer 4 (27).”