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

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

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

• i have shared the trained model of nodule detection and false positive reduction on here: https://pan.baidu.com/s/1I7zhzmPsTCbz0ZeIntNrUA ,password:orpm

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

• https://github.com/junqiangchen
• email: [email protected]
• Contact: junqiangChen
• WeChat Number: 1207173174
• WeChat Public number: 最新医学影像技术