U-Net_Lung-Segmentation

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U-Net_Lung-Segmentation

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Application of U-Net in Lung Segmentation-Pytorch

U-Net_Lung-Segmentation 최신버젼 다운로드

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# U-Net_Lung-Segmentation
Application of U-Net in Lung Segmentation 
This Implementation Achived **97% accuracy** in Lung Segmentation with U-Net
![LungSeg](https://raw.githubusercontent.com/seungjunlee96/U-Net_Lung-Segmentation/master/Figure_1.png)
 
# Dataset
Download Dataset from [Chest Xray Masks and Labels Pulmonary Chest X-Ray Defect Detection](https://www.kaggle.com/nikhilpandey360/chest-xray-masks-and-labels)

```
  /data
    /data/Lung Segmentation
      /data/Lung Segmentation/CXR_Png
      /data/Lung Segmentation/masks
```

## U-Net: Convolutional Networks for Biomedical Image Segmentation 
Writers: Olaf Ronneberger, Philipp Fischer, and Thomas Brox 
Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, LNCS, Vol.9351: 234--241, 2015, available at https://arxiv.org/pdf/1505.04597.pdf 
 
U-Net is a convolutional neural network architecture for fast and precise segmentation of images.

## U-Net Architecture
![u-net-architecture](https://raw.githubusercontent.com/seungjunlee96/U-Net_Lung-Segmentation/master/u-net-architecture.png) 
U-net architecture (example for 32x32 pixels in the lowest resolution). Each blue box corresponds to a multi-channel feature map. The number of channels is denoted on top of the box. The x-y-size is provided at the lower left edge of the box. White boxes represent copied feature maps. The arrows denote the different operations. 
Note that there is no dense layer.So images of different sizes can be used as input. 
![UnetExample](https://raw.githubusercontent.com/seungjunlee96/U-Net_Lung-Segmentation/master/UnetExample.png)

The U-Net combines **the location information from the downsampling** with **the contextual information in the upsampling** path to finally obtain a general information combining **localisation** and **context**, which is necessary to predict a good segmentation map.

### Contracting path (Downsampling)
It consists of the repeated application of : 
- Two **3x3 convolutions** (unpadded convolutions) 
- Followed by a **ReLU** (Rectified Linear Unit) and Batch Normalization
- A **2x2 max pooling** operation with stride 2 for downsampling.

### Expansive path (Upsampling)
Sequence of **up-convolutions** and **concatenation(skip-connection)** with high-resolution features from contracting path : 
- **2x2 convolution ("up-convolution")** that halves the number of feature channels
- A **concatenation** with the correspondingly cropped feature map from the contracting path
- Two **3x3 convolutions**
- Followed by a **ReLU** with Batch Normalization

### Final Bottleneck Layer
At the final layer a **1x1 convolution** is used to map each 64 componet feature vector to the desired number of classes.

## Challenges
- Very few annotated Very few annotated images available (approx. 30 per applications)
- **Touching objects** of the same class

## Contributions
- U-net learns **segmentation** in an **end-to-end** setting (beats the prior best method, a sliding-window CNN, with large margin.) 
- **excessive data augmentation** by applying **elastic deformations** which used to be the most common variation in tissue and realistic deformations can be simulated efficiently.
- Ensure Separation of Touching Objects
![TouchingObject](https://raw.githubusercontent.com/seungjunlee96/U-Net_Lung-Segmentation/master/TouchObjects.png)
- The use of **a weighted loss**, where the separating background labels between **touching cells** of the same class
- Overlap-tile strategy for seamless segmentation of arbitrary large images
![overlap_strategy](https://raw.githubusercontent.com/seungjunlee96/U-Net_Lung-Segmentation/master/Overlap-tile-strategy-for-seamless-segmentation-of-arbitrary-large-images-here.png)
 
Important trick: select the input tile size such that all 2x2 max-pooling operations are applied to a layer with an even x- and y-size

## Data Augmentations
- shift
- rotation
- random elastic deformations: 
 smooth deformations using random displacement vectors on a coarse 3 by 3 grid. 
 The displacements are sampled from a Gaussian distribution with 10 pixels standard deviation. 
 Per-pixel displacements are then computed using bicubic interpolation.![ElasticDeform](https://raw.githubusercontent.com/seungjunlee96/U-Net_Lung-Segmentation/master/ElasticDeformation.png)
- Drop-out

## Loss Function
Challenge in medical image 
: The anatomy of interest occupies **only a very small region** of the scan, which causes the learning process to **get trapped in local minima** of loss function yielding a network whose predictions are **strongly biased towards background**. 
As a result the **foreground region** is often missing or only partially detected.

- CrossEntropyLoss (Naive Method)
 Works badly for two reasons: 
 1)highly unbalanced label distribution 
 2)per-pixel intrinsic issue of cross entropy loss 
 As a result, cross entropy loss **only considers loss in a micro sense** rather than considering it globally, which is not enoujgh for image level prediction. 
 
- Dice Loss
 Originates from [Sørensen–Dice coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient), which is a statistic developed in 1940s to gauge the similarity between two samples.It was brought to computer vision community by [Milletari](https://arxiv.org/pdf/1606.04797.pdf) et al.in 2016 for 3D medical image segmentation. 
 Below shows the equation of Dice coefficient, in which *p* and *q* represent pairs of corresponding pixel values of prediction and ground truth, respectively. Its quantity range between 0 and 1 which we aim to maximize. 
![dice_loss](https://raw.githubusercontent.com/seungjunlee96/U-Net_Lung-Segmentation/master/unet_explanation/Dice_loss.png) 
 Dice loss considers the loss information **both locally and globally**, which is critical for high accuracy.
 
If you are interested in segmentation loss functions much in depth, you will find this repository helpful : https://github.com/JunMa11/SegLoss

# references
- [Understanding Dice Loss for Crisp Boundary Detection](https://towardsdatascience.com/understanding-dice-loss-for-crisp-boundary-detection-bb30c2e5f62b)

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