From 23292b82e0aabd2bc82bcddb45a4e61042960302 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dar=C3=ADo=20Here=C3=B1=C3=BA?= Date: Tue, 26 Mar 2019 09:11:05 -0300 Subject: [PATCH] Minor fix on string #68 * plus minor fixes (proposals) --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 687073b..31a2e16 100644 --- a/README.md +++ b/README.md @@ -65,7 +65,7 @@ where X denotes the last (or desired) epoch in which the model was saved. * Stochastic gradient descent and RmsProp optimizers. * Images in Matlab and Nifti format supported. * Loading of pre-trained weights at different layers. - * Connection of intermediate conv layers to the first fully connected layers (i.e. multi-scale fetures). + * Connection of intermediate conv layers to the first fully connected layers (i.e. multi-scale features). * Frequency of changes on learning rate customizable. * Note. This version includes Batch Normalization, which was not used in the Neuroimage paper. @@ -74,10 +74,10 @@ If you use this code for your research, please consider citing the original pape - Dolz J, Desrosiers C, Ben Ayed I. "[3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study."](http://www.sciencedirect.com/science/article/pii/S1053811917303324) NeuroImage (2017). -I strongly encourage to cite also the work of Kamnitsas :"Kamnitsas, Konstantinos, et al. ["Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation."](http://www.sciencedirect.com/science/article/pii/S1361841516301839) Medical Image Analysis 36 (2017): 61-78.", since this code is based on his previous work, DeepMedic. +I strongly encourage to cite also the work of Kamnitsas: "Kamnitsas, Konstantinos, et al. ["Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation."](http://www.sciencedirect.com/science/article/pii/S1361841516301839) Medical Image Analysis 36 (2017): 61-78.", since this code is based on his previous work, DeepMedic. ### Important notes -* In order to correctly run the training, the convnet needs that training labels are provided in a consecutive manner. This means that the first class must be label 0, the second class label 1, and so on. To ease this process I have included a functionality that takes all the images contained in a given folder and automatically corrects labels to be 0,1,2,etc. To do this, you should proceed as follows: +* In order to correctly run the training, the convnet needs that training labels are provided in a consecutive manner. This means that the first class must be label 0, the second class label 1, and so on. To ease this process I have included a functionality that takes all the images contained in a given folder and automatically corrects labels to be 0, 1, 2, etc. To do this, you should proceed as follows: ``` python processLabels.py ~yourpath/Training/LabelsNonCorrected ~yourpath/Training/LabelsCorrected 9 0 @@ -112,5 +112,5 @@ You can contact me at: jose.dolz.upv@gmail.com ### Other implementations: - * A version of the network in Keras has been implemented in: "joseabernal/iSEG2017"(https://github.com/joseabernal/iSeg2017-nic_vicorob) -- * Another Keras version of this network can be found in : "Deep Gray Matter (DGM) Segmentation using 3D Convolutional Neural Network: application to QSM"(https://github.com/zl376/segDGM_CNN) +- * Another Keras version of this network can be found in: "Deep Gray Matter (DGM) Segmentation using 3D Convolutional Neural Network: application to QSM"(https://github.com/zl376/segDGM_CNN)