/LUNA16-Lung-Nodule-Analysis-2016-Challenge

LUNA16-Lung-Nodule-Analysis-2016-Challenge

Primary LanguagePython

LUNA16-LUng-Nodule-Analysis-2016-Challenge

This is an example of the CT images lung nodule detection and false positive reduction from LUNA16-LUng-Nodule-Analysis-2016-Challenge

Prerequisities

The following dependencies are needed:

  • numpy >= 1.11.1
  • SimpleITK >=1.0.1
  • opencv-python >=3.3.0
  • tensorflow-gpu ==1.8.0
  • pandas >=0.20.1
  • scikit-learn >= 0.17.1

How to Use

1、Preprocess

nodule detection

  • convert annotation.csv file to image mask file:run the LUNA_mask_extraction.py
  • analyze the ct image,and get the slice thickness and window width and position:run the dataAnaly.py
  • generate lung nodule ct image and mask:run the data2dprepare.py
  • generate patch(96,96,16) lung nodule image and mask:run the data3dprepare.py
  • save lung nodule data and mask into csv file run the utils.py,like this:G:\Data\segmentation\Image/0_161....

nodule classify

  • convert candidates.csv file to nodule and not-nodule image(48,48,48):run the LUNA_node_extraction.py
  • Augment the nodule image data: run the Augmain.py
  • split data into train data(80%) and test data(20%):run the subset.py
  • save lung nodule data and label into csv file like this:1,G:\Data\classify\1_aug/0_17.npy

2、Nodule Detection

  • the VNet model

  • train and predict in the script of vnet3d_train.py and vnet3d_predict.py

3、False Positive Reducution

  • the ResVGGNet model

  • train and predict in the script of ResNet3d_train.py and ResNet3d_predict.py

4、download trained model

Result

1、Nodule Detection

  • train loss and train accuracy

  • the segment result

2、False Positive Reducution

  • train loss and train accuracy

  • ROC,Confusion Matrix and Metrics

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