This page describes how to acquire and use the network described in:
Li W., Wang G., Fidon L., Ourselin S., Cardoso M.J., Vercauteren T. (2017) On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task. In: Information Processing in Medical Imaging. IPMI 2017
This network parcellates 160 types of structures (including 155 neuroanatomical structures) from brain MR images.
The network weights and examples data can be downloaded with the command
net_download highres3dnet_brain_parcellation_model_zoo
(Replace net_download
with python net_download.py
if you cloned the NiftyNet repository.)
Generate segmentations for the included example image with the command
net_segment inference -c ~/niftynet/extensions/highres3dnet_brain_parcellation/highres3dnet_config_eval.ini
Replace net_segment
with python net_segment.py
if you cloned the NiftyNet repository.