% Plos One Journal - http://dx.doi.org/10.1371/journal.pone.0173900 % ################################## % ''Multi-channel MRI segmentation of eye structures and tumors using % patient-specific features'' % ################################## % % C. Ciller, S.I. De Zanet, K. Kamnitsas, P. Maeder, B. Glocker, % F.L. Munier, D. Rueckert, J-P. Thiran, M.B. Cuadra* and R. Sznitman* *Equally contributed authors % Copyright (c) - All rights reserved. University of Lausanne. 2016. The content of these folders include all the necessary steps for compu- ting the automatic segmentation of eye structures and tumors in 3D MRI. Upon acceptance of this manuscript, all the experiments and a working copy of the software will be made available for its use. % Requirements: %%%%%%%%%%%%%%%%%%%%% Software: - VirtualBox - Matlab R2014a and superior - Mac OS X / Linux / Windows Hardware: - 25 GB Free Disk (Virtual Image) - 4/8 GB of RAM - Nvidia (R) GPU (GTX 970 or superior) with CUDA/cuDNN capabilities. % 1_EyeSegmentation : %%%%%%%%%%%%%%%%%%%%% admin_password: plosone This section Covers the EyeModeler software for the segmentation of eye structures in 3D MRI. It contains a working copy of the software deve- loped for the automatic segmentation of eye structures in 3D MRI. For the sake of simplicity we offer a video representing the process of segmentation https://youtu.be/0n5Wz8RPQ7w % 2_PlosOne_RF_Experiments : %%%%%%%%%%%%%%%%%%%%%%%%%%%% Here we cover the first round of experiments with a Random Forest confi- guration using the output of the previous step. % 3_PlosOne_CNN_Experiments : %%%%%%%%%%%%%%%%%%%%%%%%%%%%% This section covers the procedure for training the DeepMedic CNN model. The original code can be found in: https://github.com/Kamnitsask/deepmedic The code in this section is modified to be able to cope with a varying number of input channels [6,9] and a leave-one-out training configuration % 4_EyeTumorSegment_Graphcut : %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Here we perform the final graph-cut refinement on the output of the experiments with different classifiers. For the sake of simplicity, we have configured the graph-cut optimization on the Random Forest experiments proposed in this manuscript.