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+# 3D_DenseSeg: 3D Densely Convolutional Networks for Volumetric Segmentation
+By Toan Duc Bui, Jitae Shin, Taesup Moon
+
+This is the implementation of our method in the MICCAI Grand Challenge on 6-month infant brain MRI segmentation-in conjunction with MICCAI 2017.
+
+### Update: (Aug. 14. 2019): We also release the Pytorch version of my journal version at https://github.com/tbuikr/3D-SkipDenseSeg
+
+### Citation
+```
+@article{bui2019skip,
+  title={Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation},
+  author={Bui, Toan Duc and Shin, Jitae and Moon, Taesup},
+  journal={Biomedical Signal Processing and Control},
+  volume={54},
+  pages={101613},
+  year={2019},
+  publisher={Elsevier}
+}
+```
+
+Link journal version: https://www.sciencedirect.com/science/article/pii/S1746809419301946
+
+Link conference version: https://arxiv.org/abs/1709.03199
+
+### Introduction
+6-month infant brain MRI segmentation aims to segment the brain into: White matter, Gray matter, and Cerebrospinal fluid. It is a difficult task due to larger overlapping between tissues, low contrast intensity. We treat the problem by using very deep 3D convolution neural network. Our result achieved the top performance in 6 performance metrics. 
+
+### Dice Coefficient (DC) for 9th subject
+|                   | CSF       | GM             | WM   | Average 
+|-------------------|:-------------------:|:---------------------:|:-----:|:--------------:|
+|3D-DenseSeg  | 94.74% | 91.61% |91.30% | 92.55% 
+
+### Citation
+```
+@article{bui20173d,
+  title={3D Densely Convolution Networks for Volumetric Segmentation},
+  author={Bui, Toan Duc and Shin, Jitae and Moon, Taesup},
+  journal={arXiv preprint arXiv:1709.03199},
+  year={2017}
+}
+```
+
+### Requirements: 
+- 3D-CAFFE (as below), python 2.7, Ubuntu 14.04, CUDNN 5.1, CUDA 8.0
+- TiTan X Pascal 12GB
+
+### Installation
+- Step 1: Download the source code
+```
+git clone https://github.com/tbuikr/3D_DenseSeg.git
+cd 3D_DenseSeg
+```
+- Step 2: Download dataset at `http://iseg2017.web.unc.edu/download/` and change the path of the dataset `data_path` and saved path `target_path` in file `prepare_hdf5_cutedge.py`
+```
+data_path = '/path/to/your/dataset/'
+target_path = '/path/to/your/save/hdf5 folder/'
+```
+
+- Step 3: Generate hdf5 dataset
+
+```
+python prepare_hdf5_cutedge.py
+```
+
+- Step 4: Run training
+
+```
+./run_train.sh
+```
+
+- Step 5: Generate score map and segmentation image. You have to change the path in the file `seg_deploy.py` as 
+```data_path = '/path/to/your/dataset/'
+caffe_root = '/path/to/your/caffe/build/tools/caffe/'# (i.e '/home/toanhoi/caffe/build/tools/caffe/')
+```
+
+And run
+```
+python seg_deploy.py
+```
+
+### 3D CAFFE
+For CAFFE, we use 3D UNet CAFFE with minor modification. Hence, you first download the 3D UNet CAFFE at
+
+`https://lmb.informatik.uni-freiburg.de/resources/opensource/unet.en.html`
+
+And run the installation as the README file. Then we change the HDF5DataLayer that allows to randomly crop patch based on the code at `https://github.com/yulequan/3D-Caffe`
+You can download the code by
+```
+git clone https://github.com/yulequan/3D-Caffe/
+cd 3D-Caffe
+git checkout 3D-Caffe
+cd ../
+```
+
+After downloading both source codes, we have two folder code `3D-Caffe` and `caffe` (for 3D UNet CAFFE). We have to copy the hdf5 data files from `3D-Caffe` to `caffe` by the commands
+
+```
+cp ./3D-Caffe/src/caffe/layers/hdf5_data_layer.cpp ./caffe/src/caffe/layers/
+cp ./3D-Caffe/src/caffe/layers/hdf5_data_layer.cu ./caffe/src/caffe/layers/
+cp ./3D-Caffe/include/caffe/layers/hdf5_data_layer.hpp ./caffe/include/caffe/layers/hdf5_data_layer.hpp
+```
+
+Then add these lines in the field `message TransformationParameter` of the file  `caffe.proto` in the `./caffe/src/caffe/proto`
+ (3D UNet CAFFE)
+```
+optional uint32 crop_size_w = 8 [default = 0];
+optional uint32 crop_size_h = 9 [default = 0];
+optional uint32 crop_size_l = 10 [default = 0];
+```
+
+Add following code in the `./caffe/include/caffe/filler.hpp`
+
+```
+/**
+  3D bilinear filler 
+*/
+template <typename Dtype>
+class BilinearFiller_3D : public Filler<Dtype> {
+ public:
+  explicit BilinearFiller_3D(const FillerParameter& param)
+      : Filler<Dtype>(param) {}
+  virtual void Fill(Blob<Dtype>* blob) {
+    CHECK_EQ(blob->num_axes(), 5) << "Blob must be 5 dim.";
+    CHECK_EQ(blob->shape(-1), blob->shape(-2)) << "Filter must be square";
+    CHECK_EQ(blob->shape(-2), blob->shape(-3)) << "Filter must be square";
+    Dtype* data = blob->mutable_cpu_data();
+
+    int f = ceil(blob->shape(-1) / 2.);
+    float c = (2 * f - 1 - f % 2) / (2. * f);
+    for (int i = 0; i < blob->count(); ++i) {
+      float x = i % blob->shape(-1);
+      float y = (i / blob->shape(-1)) % blob->shape(-2);
+      float z = (i/(blob->shape(-1)*blob->shape(-2))) % blob->shape(-3);
+      data[i] = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c)) * (1-fabs(z / f - c));
+    }
+    
+
+    CHECK_EQ(this->filler_param_.sparse(), -1)
+         << "Sparsity not supported by this Filler.";
+  }
+};
+```
+
+and in the `GetFiller(const FillerParameter& param)` function (same file)
+
+```
+else if (type == "bilinear_3D"){
+    return new BilinearFiller_3D<Dtype>(param);
+  }
+ ```
+
+Final, you recompile 3D UNet CAFFE (uncomment `USE_CUDNN := 1`) and can you my prototxt. Please cite these papers when you use the CAFFE code
+
+###
+### Note
+- If you want to generate network prototxt, you have to change the path of `caffe_root`
+```
+caffe_root = '/path/to/your/caffe/build/tools/caffe/'# (i.e '/home/toanhoi/caffe/build/tools/caffe/')
+```
+And run
+```
+python make_3D_DenseSeg.py
+```
+- If you have the error `AttributeError: 'LayerParameter' object has no attribute 'shuffle'` when run  `python make_3D_DenseSeg.py`, then you can fix it by replacing the line 35 in the `net_spec.py`:
+```
+  #param_names = [s for s in dir(layer) if s.endswith('_param')]
+  param_names = [f.name for f in layer.DESCRIPTOR.fields if f.name.endswith('_param')]
+  ```
+- Plot training loss during training
+
+```
+python plot_trainingloss.py ./log/3D_DenseSeg.log 
+```
+