Prepare for UNet++ pytorch version

README.md

@@ -2,7 +2,7 @@
 
 UNet++ is a new general purpose image segmentation architecture for more accurate image segmentation. UNet++ consists of U-Nets of varying depths whose decoders are densely connected at the same resolution via the redesigned skip pathways, which aim to address two key challenges of the U-Net: 1) unknown depth of the optimal architecture and 2) the unnecessarily restrictive design of skip connections.
 
-# Paper
+## Paper
 
 This repository provides the official Keras implementation of UNet++ in the following papers:
 
@@ -18,126 +18,20 @@ Arizona State University <br/>
 Deep Learning in Medical Image Analysis ([DLMIA](https://cs.adelaide.edu.au/~dlmia4/)) 2018. **(Oral)** <br/>
 [paper](https://arxiv.org/abs/1807.10165) | [code](https://github.com/MrGiovanni/Nested-UNet) | [slides](https://docs.wixstatic.com/ugd/deaea1_1d1e512ebedc4facbb242d7a0f2b7a0b.pdf) | [poster](https://docs.wixstatic.com/ugd/deaea1_993c14ef78f844c88a0dae9d93e4857c.pdf) | [blog](https://zhuanlan.zhihu.com/p/44958351)
 
+## Official implementation
 
-# Other implementation
+- keras/
+- pytorch/
+
+## Other implementation
 - [[PyTorch](https://github.com/4uiiurz1/pytorch-nested-unet)] (by 4ui_iurz1)
 - [[PyTorch](https://towardsdatascience.com/biomedical-image-segmentation-unet-991d075a3a4b)] (by Hong Jing)
 - [[PyTorch](https://github.com/ZJUGiveLab/UNet-Version)] (by ZJUGiveLab)
 - [[Keras](https://www.kaggle.com/meaninglesslives/nested-unet-with-efficientnet-encoder)] (by Siddhartha)
 
-# What is in this repository
-
-### 1. Available architectures
- - [U-Net](https://arxiv.org/abs/1505.04597)
- - [DLA](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Deep_Layer_Aggregation_CVPR_2018_paper.pdf)
- - **[UNet++](https://link.springer.com/chapter/10.1007/978-3-030-00889-5_1)**
- - [FPN](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf)
- - [Linknet](https://arxiv.org/abs/1707.03718)
- - [PSPNet](https://arxiv.org/abs/1612.01105)
-
-### 2. Available backbones
-| Backbone model      |Name| Weights    |
-|---------------------|:--:|:------------:|
-| VGG16               |`vgg16`| `imagenet` |
-| VGG19               |`vgg19`| `imagenet` |
-| ResNet18            |`resnet18`| `imagenet` |
-| ResNet34            |`resnet34`| `imagenet` |
-| ResNet50            |`resnet50`| `imagenet`<br>`imagenet11k-places365ch` |
-| ResNet101           |`resnet101`| `imagenet` |
-| ResNet152           |`resnet152`| `imagenet`<br>`imagenet11k` |
-| ResNeXt50           |`resnext50`| `imagenet` |
-| ResNeXt101          |`resnext101`| `imagenet` |
-| DenseNet121         |`densenet121`| `imagenet` |
-| DenseNet169         |`densenet169`| `imagenet` |
-| DenseNet201         |`densenet201`| `imagenet` |
-| Inception V3        |`inceptionv3`| `imagenet` |
-| Inception ResNet V2 |`inceptionresnetv2`| `imagenet` |
-
-# How to use UNet++
-
-### 1. Requirements
-Python 3.x, Keras 2.2.2, Tensorflow 1.4.1 and other common packages listed in `requirements.txt`.
-
-### 2. Installation
-
-```bash
-git clone https://github.com/MrGiovanni/UNetPlusPlus.git
-cd UNetPlusPlus
-pip install -r requirements.txt
-git submodule update --init --recursive
-```
-
-### 3. Running the scripts
-
-#### Application 1: [Data Science Bowl 2018](https://www.kaggle.com/c/data-science-bowl-2018)
-```bash
-CUDA_VISIBLE_DEVICES=0 python DSB2018_application.py --run 1 \
-                                                     --arch Xnet \
-                                                     --backbone vgg16 \
-                                                     --init random \
-                                                     --decoder transpose \
-                                                     --input_rows 96 \
-                                                     --input_cols 96 \
-                                                     --input_deps 3 \
-                                                     --nb_class 1 \
-                                                     --batch_size 2048 \
-                                                     --weights None \
-                                                     --verbose 1
-```
-#### Application 2: [Liver Tumor Segmentation Challenge (LiTS)](https://competitions.codalab.org/competitions/17094)
-
-#### Application 3: [Polyp Segmentation (ASU-Mayo)](https://polyp.grand-challenge.org/databases/)
-
-#### Application 4: [Lung Image Database Consortium image collection (LIDC-IDRI)](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)
-
-#### Application 5: [Multiparametric Brain Tumor Segmentation (BRATS 2013)](https://www.smir.ch/BRATS/Start2013)
-```bash
-CUDA_VISIBLE_DEVICES=0 python BRATS2013_application.py --run 1 \
-                                                     --arch Xnet \
-                                                     --backbone vgg16 \
-                                                     --init random \
-                                                     --decoder transpose \
-                                                     --input_rows 256 \
-                                                     --input_cols 256 \
-                                                     --input_deps 3 \
-                                                     --nb_class 1 \
-                                                     --batch_size 2048 \
-                                                     --weights None \
-                                                     --verbose 1
-```
-
-# Code examples for your own data
-
-Train a UNet++ structure (`Xnet` in the code):  
-```python
-from segmentation_models import Unet, Nestnet, Xnet
-
-# prepare data
-x, y = ... # range in [0,1], the network expects input channels of 3
-
-# prepare model
-model = Xnet(backbone_name='resnet50', encoder_weights='imagenet', decoder_block_type='transpose') # build UNet++
-# model = Unet(backbone_name='resnet50', encoder_weights='imagenet', decoder_block_type='transpose') # build U-Net
-# model = NestNet(backbone_name='resnet50', encoder_weights='imagenet', decoder_block_type='transpose') # build DLA
-
-model.compile('Adam', 'binary_crossentropy', ['binary_accuracy'])
-
-# train model
-model.fit(x, y)
-```
 
-# To do
-- [x] Add VGG backbone for UNet++
-- [x] Add ResNet backbone for UNet++
-- [x] Add ResNeXt backbone for UNet++
-- [ ] Add DenseNet backbone for UNet++
-- [ ] Add Inception backbone for UNet++
-- [ ] Add [Tiramisu](https://arxiv.org/pdf/1611.09326.pdf]) and Tiramisu++
-- [ ] Add FPN++
-- [ ] Add Linknet++
-- [ ] Add PSPNet++
 
-# Citation
+## Citation
 If you use UNet++ for your research, please cite our papers:
 ```
 @article{zhou2019unetplusplus,
@@ -158,6 +52,6 @@ If you use UNet++ for your research, please cite our papers:
 }
 ```
 
-# Acknowledgments
+## Acknowledgments
 
-This repository has been built upon [qubvel/segmentation_models](https://github.com/qubvel/segmentation_models). We appreciate the effort of Pavel Yakubovskiy for providing well-organized segmentation models to the community. This research has been supported partially by NIH under Award Number R01HL128785, by ASU and Mayo Clinic through a Seed Grant and an Innovation Grant. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. This is a patent-pending technology.
+This research has been supported partially by NIH under Award Number R01HL128785, by ASU and Mayo Clinic through a Seed Grant and an Innovation Grant. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. This is a patent-pending technology.

keras/README.md

@@ -0,0 +1,153 @@
+# UNet++: Official Keras Implementation
+
+This repository provides the official **Keras** implementation of UNet++ in the following papers:
+
+**UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation** <br/>
+[Zongwei Zhou](https://www.zongweiz.com), [Md Mahfuzur Rahman Siddiquee](https://github.com/mahfuzmohammad), [Nima Tajbakhsh](https://www.linkedin.com/in/nima-tajbakhsh-b5454376/), and [Jianming Liang](https://chs.asu.edu/jianming-liang) <br/>
+Arizona State University <br/>
+IEEE Transactions on Medical Imaging ([TMI](https://ieee-tmi.org/)) <br/>
+[paper](https://arxiv.org/abs/1912.05074) | [code](https://github.com/MrGiovanni/Nested-UNet)
+
+**UNet++: A Nested U-Net Architecture for Medical Image Segmentation** <br/>
+[Zongwei Zhou](https://www.zongweiz.com), [Md Mahfuzur Rahman Siddiquee](https://github.com/mahfuzmohammad), [Nima Tajbakhsh](https://www.linkedin.com/in/nima-tajbakhsh-b5454376/), and [Jianming Liang](https://chs.asu.edu/jianming-liang) <br/>
+Arizona State University <br/>
+Deep Learning in Medical Image Analysis ([DLMIA](https://cs.adelaide.edu.au/~dlmia4/)) 2018. **(Oral)** <br/>
+[paper](https://arxiv.org/abs/1807.10165) | [code](https://github.com/MrGiovanni/Nested-UNet) | [slides](https://docs.wixstatic.com/ugd/deaea1_1d1e512ebedc4facbb242d7a0f2b7a0b.pdf) | [poster](https://docs.wixstatic.com/ugd/deaea1_993c14ef78f844c88a0dae9d93e4857c.pdf) | [blog](https://zhuanlan.zhihu.com/p/44958351)
+
+
+## What is in this repository
+
+### 1. Available architectures
+ - [U-Net](https://arxiv.org/abs/1505.04597)
+ - [DLA](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Deep_Layer_Aggregation_CVPR_2018_paper.pdf) (UNet+)
+ - **[UNet++](https://link.springer.com/chapter/10.1007/978-3-030-00889-5_1)**
+ - [FPN](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf)
+ - [Linknet](https://arxiv.org/abs/1707.03718)
+ - [PSPNet](https://arxiv.org/abs/1612.01105)
+
+### 2. Available backbones
+| Backbone model      |Name| Weights    |
+|---------------------|:--:|:------------:|
+| VGG16               |`vgg16`| `imagenet` |
+| VGG19               |`vgg19`| `imagenet` |
+| ResNet18            |`resnet18`| `imagenet` |
+| ResNet34            |`resnet34`| `imagenet` |
+| ResNet50            |`resnet50`| `imagenet`<br>`imagenet11k-places365ch` |
+| ResNet101           |`resnet101`| `imagenet` |
+| ResNet152           |`resnet152`| `imagenet`<br>`imagenet11k` |
+| ResNeXt50           |`resnext50`| `imagenet` |
+| ResNeXt101          |`resnext101`| `imagenet` |
+| DenseNet121         |`densenet121`| `imagenet` |
+| DenseNet169         |`densenet169`| `imagenet` |
+| DenseNet201         |`densenet201`| `imagenet` |
+| Inception V3        |`inceptionv3`| `imagenet` |
+| Inception ResNet V2 |`inceptionresnetv2`| `imagenet` |
+
+## How to use UNet++
+
+### 1. Requirements
+Python 3.x, Keras 2.2.2, Tensorflow 1.4.1 and other common packages listed in `requirements.txt`.
+
+### 2. Installation
+
+```bash
+git clone https://github.com/MrGiovanni/UNetPlusPlus.git
+cd UNetPlusPlus/keras
+pip install -r requirements.txt
+git submodule update --init --recursive
+```
+
+### 3. Running the scripts
+
+#### Application 1: [Data Science Bowl 2018](https://www.kaggle.com/c/data-science-bowl-2018)
+```bash
+CUDA_VISIBLE_DEVICES=0 python DSB2018_application.py --run 1 \
+                                                     --arch Xnet \
+                                                     --backbone vgg16 \
+                                                     --init random \
+                                                     --decoder transpose \
+                                                     --input_rows 96 \
+                                                     --input_cols 96 \
+                                                     --input_deps 3 \
+                                                     --nb_class 1 \
+                                                     --batch_size 2048 \
+                                                     --weights None \
+                                                     --verbose 1
+```
+#### Application 2: [Liver Tumor Segmentation Challenge (LiTS)](https://competitions.codalab.org/competitions/17094)
+
+#### Application 3: [Polyp Segmentation (ASU-Mayo)](https://polyp.grand-challenge.org/databases/)
+
+#### Application 4: [Lung Image Database Consortium image collection (LIDC-IDRI)](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)
+
+#### Application 5: [Multiparametric Brain Tumor Segmentation (BRATS 2013)](https://www.smir.ch/BRATS/Start2013)
+```bash
+CUDA_VISIBLE_DEVICES=0 python BRATS2013_application.py --run 1 \
+                                                     --arch Xnet \
+                                                     --backbone vgg16 \
+                                                     --init random \
+                                                     --decoder transpose \
+                                                     --input_rows 256 \
+                                                     --input_cols 256 \
+                                                     --input_deps 3 \
+                                                     --nb_class 1 \
+                                                     --batch_size 2048 \
+                                                     --weights None \
+                                                     --verbose 1
+```
+
+## Code examples for your own data
+
+Train a UNet++ structure (`Xnet` in the code):  
+```python
+from segmentation_models import Unet, Nestnet, Xnet
+
+# prepare data
+x, y = ... # range in [0,1], the network expects input channels of 3
+
+# prepare model
+model = Xnet(backbone_name='resnet50', encoder_weights='imagenet', decoder_block_type='transpose') # build UNet++
+# model = Unet(backbone_name='resnet50', encoder_weights='imagenet', decoder_block_type='transpose') # build U-Net
+# model = NestNet(backbone_name='resnet50', encoder_weights='imagenet', decoder_block_type='transpose') # build DLA
+
+model.compile('Adam', 'binary_crossentropy', ['binary_accuracy'])
+
+# train model
+model.fit(x, y)
+```
+
+## To do
+- [x] Add VGG backbone for UNet++
+- [x] Add ResNet backbone for UNet++
+- [x] Add ResNeXt backbone for UNet++
+- [ ] Add DenseNet backbone for UNet++
+- [ ] Add Inception backbone for UNet++
+- [ ] Add [Tiramisu](https://arxiv.org/pdf/1611.09326.pdf]) and Tiramisu++
+- [ ] Add FPN++
+- [ ] Add Linknet++
+- [ ] Add PSPNet++
+
+## Citation
+If you use UNet++ for your research, please cite our papers:
+```
+@article{zhou2019unetplusplus,
+  title={UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation},
+  author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
+  journal={IEEE Transactions on Medical Imaging},
+  year={2019},
+  publisher={IEEE}
+}
+
+@incollection{zhou2018unetplusplus,
+  title={Unet++: A Nested U-Net Architecture for Medical Image Segmentation},
+  author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
+  booktitle={Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support},
+  pages={3--11},
+  year={2018},
+  publisher={Springer}
+}
+```
+
+## Acknowledgments
+
+This repository has been built upon [qubvel/segmentation_models](https://github.com/qubvel/segmentation_models). We appreciate the effort of Pavel Yakubovskiy for providing well-organized segmentation models to the community. This research has been supported partially by NIH under Award Number R01HL128785, by ASU and Mayo Clinic through a Seed Grant and an Innovation Grant. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. This is a patent-pending technology.

pytorch/README.md

@@ -0,0 +1,71 @@
+# UNet++: Official PyTorch Implementation
+
+This repository provides the official **PyTorch** implementation of UNet++ in the following papers:
+
+**UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation** <br/>
+[Zongwei Zhou](https://www.zongweiz.com), [Md Mahfuzur Rahman Siddiquee](https://github.com/mahfuzmohammad), [Nima Tajbakhsh](https://www.linkedin.com/in/nima-tajbakhsh-b5454376/), and [Jianming Liang](https://chs.asu.edu/jianming-liang) <br/>
+Arizona State University <br/>
+IEEE Transactions on Medical Imaging ([TMI](https://ieee-tmi.org/)) <br/>
+[paper](https://arxiv.org/abs/1912.05074) | [code](https://github.com/MrGiovanni/Nested-UNet)
+
+**UNet++: A Nested U-Net Architecture for Medical Image Segmentation** <br/>
+[Zongwei Zhou](https://www.zongweiz.com), [Md Mahfuzur Rahman Siddiquee](https://github.com/mahfuzmohammad), [Nima Tajbakhsh](https://www.linkedin.com/in/nima-tajbakhsh-b5454376/), and [Jianming Liang](https://chs.asu.edu/jianming-liang) <br/>
+Arizona State University <br/>
+Deep Learning in Medical Image Analysis ([DLMIA](https://cs.adelaide.edu.au/~dlmia4/)) 2018. **(Oral)** <br/>
+[paper](https://arxiv.org/abs/1807.10165) | [code](https://github.com/MrGiovanni/Nested-UNet) | [slides](https://docs.wixstatic.com/ugd/deaea1_1d1e512ebedc4facbb242d7a0f2b7a0b.pdf) | [poster](https://docs.wixstatic.com/ugd/deaea1_993c14ef78f844c88a0dae9d93e4857c.pdf) | [blog](https://zhuanlan.zhihu.com/p/44958351)
+
+
+## What is in this repository
+
+### 1. Available architectures
+ - [3D U-Net](https://arxiv.org/abs/1505.04597)
+ - **[3D UNet++](https://link.springer.com/chapter/10.1007/978-3-030-00889-5_1)**
+
+
+## How to use UNet++
+
+### 1. Requirements
+Linux, Python 3.7+, PyTorch 1.6+ and other common packages listed in `requirements.txt`.
+
+### 2. Installation
+
+
+### 3. Running the scripts
+
+#### Application 1: Liver and tumour segmentation in [Medical Segmentation Decathlon](http://medicaldecathlon.com/)
+
+| experiment      | Liver 1_Dice | Liver 2_Dice | 
+|---------------------|:--:|:------------:|
+| U-Net (reported)               | 95.71 |  63.72  |
+| U-Net (reproduced)          |  | 62.61 |
+| UNet++            | 96.11 |  66.25  |
+
+
+## Code examples for your own data
+
+
+
+## Citation
+If you use UNet++ for your research, please cite our papers:
+```
+@article{zhou2019unetplusplus,
+  title={UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation},
+  author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
+  journal={IEEE Transactions on Medical Imaging},
+  year={2019},
+  publisher={IEEE}
+}
+
+@incollection{zhou2018unetplusplus,
+  title={Unet++: A Nested U-Net Architecture for Medical Image Segmentation},
+  author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
+  booktitle={Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support},
+  pages={3--11},
+  year={2018},
+  publisher={Springer}
+}
+```
+
+## Acknowledgments
+
+We thank [Shivam Bajpai](https://github.com/sbajpai2) for implementating UNet++ and participating in the Medical Segmentation Decathlon challenge. This repository has been built upon [MIC-DKFZ/nnUNet](https://github.com/MIC-DKFZ/nnUNet). We appreciate the effort of Fabian Isensee for providing competitive segmentation framework to the community. This research has been supported partially by NIH under Award Number R01HL128785, by ASU and Mayo Clinic through a Seed Grant and an Innovation Grant. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. This is a patent-pending technology.