DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images

Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be tedious and time-consuming task for expert annotators.

Data and Resources

Cite this as

Andres Diaz-Pinto, Pritesh Mehta, Sachidanand Alle, Muhammad Asad, Richard Brown, Vishwesh Nath, Alvin Ihsani, Michela Antonelli, Daniel Palkovics, Csaba Pinter, Ron Alkalay, Steve Pieper, Holger R. Roth, Daguang Xu, Prerna Dogra, Tom Vercauteren, Andrew Feng, Abood Quraini, Sebastien Ourselin, M. Jorge Cardoso (2024). Dataset: DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images. https://doi.org/10.57702/87fb6wr0

DOI retrieved: December 16, 2024

Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.1007/978-3-031-17027-0_2
Author Andres Diaz-Pinto
More Authors
Pritesh Mehta
Sachidanand Alle
Muhammad Asad
Richard Brown
Vishwesh Nath
Alvin Ihsani
Michela Antonelli
Daniel Palkovics
Csaba Pinter
Ron Alkalay
Steve Pieper
Holger R. Roth
Daguang Xu
Prerna Dogra
Tom Vercauteren
Andrew Feng
Abood Quraini
Sebastien Ourselin
M. Jorge Cardoso
Homepage https://github.com/Project-MONAI/MONAILabel