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KiTS19——2019 Kidney Tumor Segmentation Challenge

This is an example of the CT images Kidney Tumor Segmentation

Prerequisities

The following dependencies are needed:

  • python == 3.5.5
  • 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
  • json >=2.0.9

How to Use

1、Preprocess

  • analyze the ct image,and get the slice thickness and window width and position:run the dataAnaly.py

1.1 Preprocess Kidney

  • keep kidney region into fixed size(512,512,64) for Corse Kidney Segmentation:run the corsedata2dprepare.py
  • generate patch(128,128,64) kidney image and mask for Corse Kidney Segmentation:run the corsedata3dprepare.py
  • keep Kidney region range image for fine Kidney Segmentation:run the finedata2dprepare.py
  • generate patch(128,128,64) kidney image and mask for fine Kidney Segmentation:run the finedata3dprepare.py
  • save patch image and mask into csv file: run the utils.py,like file trainSegmentation.csv
  • split trainSegmentation.csv into training set and test set:run subset.py

1.2 Preprocess Kidney Tumor

  • generate tumor image and mask for 2d Kidney Tumor Segmentation:run the tumordata2dprepare.py
  • generate tumor image and mask for 3d Kidney Tumor Segmentation:run the finedata3dprepare.py
  • save tumor image and mask path into csv file: run the utils.py,like file traintumorSegmentation.csv
  • split traintumorSegmentation.csv into training set and test set

2、Kidney Segmentation

  • the VNet model

2.1 Corse Kidney Segmentation

  • Corse Kidney Segmentation training:run the train_vnet3d_kidney_corse.py
  • Corse Kidney Segmentation inference:run the inference_vnet3d_kidney_corse.py
  • this step get Corse Kidney range,can find the start and end pos in the kidneyrang.txt

2.2 Fine Kidney Segmentation

  • Fine Kidney Segmentation training:run the train_vnet3d_kidney_fine.py
  • Fine Kidney Segmentation inference:run the inference_vnet3d_kidney_fine.py
  • this step following the 2.1 result,get fine Kidney result

2.3 Fine Kidney Segmentation

  • remove Kidney Segmentation small object:run the segresultprocess.py removekidneysmallobj function

3、Kidney Tumor Segmentation

  • the VNet2d model

3.1 2d Kidney Tumor Segmentation

  • 2d Kidney Tumor Segmentation training:run the train_vnet2d_tumor.py

  • 2d Kidney Tumor Segmentation inference:run the inference_vnet2d_tumor.py

  • this step get 2d slice tumor result

  • the VNet3d model

3.2 3d Kidney Tumor Segmentation

  • 3d Kidney Tumor Segmentation training:run the train_vnet3d_tumor.py
  • 3d Kidney Tumor Segmentation inference:run the inference_vnet3d_tumor.py
  • this step get 3d tumor result

3.3 Kidney Tumor Result Process

  • remove Kidney Tumor Segmentation small object:run the segresultprocess.py remove2d3dtumorsmallobj function
  • calculate overlap between 2d tumor and 3d tumor reslut.
  • save the region of 2d tumor and 3d tumor reslut that connect overlap region.
  • save the region of 2d tumor and 3d tumor within Kidney result.
  • merge the above two result and get final tumor result
  • all above step can find in the segresultprocess.py

Result

1、Kidney Segmentation

  • the train loss

  • 200-209case dice value and result

2、Kidney Tumor Segmentation

  • the 2dtrain loss

  • the 3dtrain loss

3、Lead Board

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