Hierarchical 3D fully convolutional networks for multi-organ segmentation

A two-stage, coarse-to-fine approach that trains an FCN model to roughly delineate the organs of interest in the first stage and then uses these predictions of the first-stage FCN to define a candidate region that will be used to train a second FCN.

Data and Resources

Cite this as

Holger R. Roth, Hirohisa Oda, Yuichiro Hayashi, Masahiro Oda, Natsuki Shimizu, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori (2024). Dataset: Hierarchical 3D fully convolutional networks for multi-organ segmentation. https://doi.org/10.57702/h41e0ga7

DOI retrieved: December 3, 2024

Additional Info

Field Value
Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.1704.06382
Author Holger R. Roth
More Authors
Hirohisa Oda
Yuichiro Hayashi
Masahiro Oda
Natsuki Shimizu
Michitaka Fujiwara
Kazunari Misawa
Kensaku Mori
Homepage https://arxiv.org/abs/1702.00045