You're currently viewing an old version of this dataset. To see the current version, click here.

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

This dataset has no data

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

Private DOI This DOI is not yet resolvable.
It is available for use in manuscripts, and will be published when the Dataset is made public.

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