Awesome

Applications of Foundation Models in Photoacoustic Image Segmentation

Installing Dependencies

1 Python

• This code requires python>=3.8, as well as pytorch>=1.7 and torchvision>=0.8. Click here to ensure the correct installation of PyTorch and TorchVision dependencies.

• Install gdal:

  conda install -c conda-forge gdal
  

• Create a local git repository:

  git clone git@github.com:Adi-Deng/photoacoustic-SAM.git
  

2 Matlab

• Matlab version: Matlab R2021a

Mouse Outer Contour Segmentation

• Open the SAM_segmentation folder: cd SAM_segmentation;
• Set the prompt: Change the input_point and input_label variables in seg_multiple_prompt.py;
• Segmentation: python seg_multiple_prompt.py;

Photoacoustic Multiple Vessel Segmentation

• Place the images to be segmented in the \SAM_segmentation\result9 directory;
• Open the SAM_segmentation folder: cd SAM_segmentation;
• Segmentation: python seg_whole_picture.py;
• Open the multi_vessel_seg folder;
• Run vessel_segmentation.m;

3D Reconstruction

• Place the images to be segmented in the \data\jpg3d directory;
• Open the SAM_segmentation folder: cd SAM_segmentation;
• Segmentation: python seg_3d.py;
• Open the 3D_reconstruction folder, run 3DReconstruction.m;

Mouse Dual Speed of Sound Reconstruction

1 Single Speed of Sound Reconstruction

• Place the .mat files of the Sinogram to be reconstructed in the data path;
• Open the double_mouse_photoacoustic folder;
• Modify the required parameters, run mouse_1sos_reconstruction.m

2 SAM Segmentation

• Open the SAM_segmentation folder: cd SAM_segmentation;
• Convert the single speed of sound reconstructed .mat file to .png format: python mat2png.py;
• Set the prompt and segmentation: python seg_multiple_prompt.py;

3 Feature Extraction Based on SAM Segmentation Mask and Dual Speed of Sound Reconstruction

• Open the double_mouse_photoacoustic folder;

• Modify the required speed of sound and parameters, run mouse_2sos_reconstruction.m;

  Contributors

  This project was made possible with the help of many contributors (alphabetical): Handi Deng, Wubin Fu, Yucheng Zhou,Jiaxuan Xiang, Yan Luo, Xuanhao Wang. Special thanks to Segment-Anything for foundation model and RS迷途小书童 for contributions made in model deployment