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+## Overview
+This framework is provided to perform pixel-level segmentation of human liver, spleen, pancreas and kidney, based on MR images provided by German National Cohort(NAKO Dataset), using deep-learning method, and visualized the results. It establishes all functionality needed to operate on 3D images with a patch-based architecture.  
+
+NAKO Dataset：  
+- Over 3400 labeled MRI images from thousands patients
+- Over 500 MRI images for evaluation  
+
+<img src="imgs/overview.png" width="30%">  
+
+Used network architectures including 3d u-net, non-local neural network, attention u-net are proposed.  
+
+Arxiv:  
+
+## Installation
+
+use pip3 (with a venv)
+
+    pip3 install -e .
+
+if it fails consider
+
+    pip3 install -e . --user
+    
+## Usage
+
+For training use
+
+    nohup python3 -u train.py > file_out 2> file_err &
+    
+For prediction use
+
+    nohup python3 -u evaluate.py > file_out 2> file_err &
+
+## Algorithm
+### non-local neural network
+Inspired by the popular NLP Transformer architecture proposed by Google in 2017, an architecture of similar idea is proposed for image processing, the non-local neural networks.  
+
+It can capture the long-range dependencies between pixels more properly, check the paper from Wang Xiaolong https://arxiv.org/abs/1711.07971  
+
+Its architecture as following:  
+
+<img src="imgs/non-local.PNG" width="70%">
+
+### 3d U-net as baseline
+Baseline architecture is a 4-stages 3d u-net, as following:  
+
+<img src="imgs/u-net.PNG" width="70%">  
+
+## Results
+Achieve an average accurancy of 97% of all classes.  
+
+<img src="imgs/results1.png" width="60%">  
+
+<img src="imgs/exp001shape.PNG" width="50%">