This repository provides the code and models files for multi-organ segmentation in abdominal CT using cascaded 3D U-Net models. The models are described in:
"Hierarchical 3D fully convolutional networks for multi-organ segmentation" Holger R. Roth, Hirohisa Oda, Yuichiro Hayashi, Masahiro Oda, Natsuki Shimizu, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori https://arxiv.org/abs/1704.06382
This work is based on the open-source implementation of 3D U-Net: https://lmb.informatik.uni-freiburg.de/resources/opensource/unet.en.html We thank the authors for providing their implementation.
Olaf Ronneberger, Philipp Fischer & Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, LNCS, Vol.9351, 234--241, 2015 DOI Code and Özgün Çiçek, Ahmed Abdulkadir, S. Lienkamp, Thomas Brox & Olaf Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, LNCS, Vol.9901, 424--432, Oct 2016
3D U-Net is based on Caffe. To compile, follow the Caffe instructions: http://caffe.berkeleyvision.org/installation.html#prequequisites
To run the segmentation algorithm on a new case use: python run_full_cascade_deploy.py Note, please update the paths in run_full_cascade_deploy.py
You might have to add a -2000 offset to win_min/max1/2 in deploy_cascade.py if your images are in Hounsfield units.
For training, please follow the 3D U-Net instruction. prepare_data.py can be useful for converting nifti images and label images to h5 containers which can be read by caffe.
We also provide a model fine-tuned from the abdominal model based on the VISCERAL data set [1]. All related code and models are provided in the "VISCERAL" subfolder. This folder also contains *.sh scripts for fine-tuning the different stages of the cascade. train.sh is for training the model from scratch. The data list files in models/3dUnet_Visceral_with_BN.prototxt need to be updated accordingly. For more details, please refer to VISCERAL/JAMIT2017_rothhr_manuscript.pdf
Please contact Holger Roth (rothhr@mori.m.is.nagoya-u.ac.jp) for any questions.
[1] Jimenez-del-Toro, O., Müller, H., Krenn, M., Gruenberg, K., Taha, A. A., Winterstein, M., et al. Kontokotsios, G. (2016). Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks. IEEE Transactions Imaging, 35(11), 2459-2475. (http://www.visceral.eu/benchmarks/anatomy3-open/)