Revision as of 19:43, 14 March 2020

2D Example Meshes

The directory containing the 2D input data is located in the 2D folder of Medical_Imaging_Data.

The image data were acquired from Centers for Disease Control and Prevention

COVID-19-23354

• Input image : Dimensions (3,000x2,000) with spacing (1x1)
• Uniform with Min-Edge = 15: 82,981 triangles
• Adaptive with Min-Edge = 15: 67,920 triangles

Commands to generate meshes :

Uniform with Min-Edge = 15 : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./Medical_Imaging_Data/2D/COVID-19-23354.tif --uniform --min-edge 15 --output ./COVID-19-23354,uniform,e=15.vtk

Adaptive with Min-Edge = 15 : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./Medical_Imaging_Data/2D/COVID-19-23354.tif --min-edge 15 --output ./COVID-19-23354,w=0.1,e=15.vtk

Mesh generated based on the images published at (put the link). For the uniform meshes, the edge-size corresponds to 15 pixels. The adaptive meshes were created by controlling the size of the elements based on the difference in the intensity of the pixels

COVID-19-23311

• Input image : Dimensions (2,460x2,460) with spacing (1x1)
• Uniform with Min-Edge = 20: 47,028 triangles
• Adaptive with Min-Edge = 20: 34,080 tetrahedra

Commands to generate meshes :

Uniform with Min-Edge = 20 : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./Medical_Imaging_Data/2D/COVID-19-23311.tif --uniform --min-edge 20 --output ./COVID-19-23311,uniform,e=20.vtk

Adaptive with Min-Edge = 20 : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./Medical_Imaging_Data/2D/COVID-19-23311.tif --min-edge 20 --output ./COVID-19-23311,w=0.1,e=20.vtk

3D Example Meshes

The directory containing the 3D input data is located in the 3D folder of Medical_Imaging_Data.

Head-Neck

• Input image : Dimensions (255x255x229) with spacing (0.976562x0.976562x1.40002)
• Uniform with Delta = default: 205,416 tetrahedra
• Uniform with Delta = 1.5: 770,853 tetrahedra

Commands to generate meshes :

Uniform with Delta = default : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Head-Neck.mha --output ./Head-Neck,d=2.49023.vtk

Uniform with Delta = 1.5 : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Head-Neck.mha --delta 1.5 --output ./Head-Neck,d=1.5.vtk

Brain-With-Tumor-Case17

• Input image : Dimensions (448x512x176) with spacing (0.488281x0.488281x1)
• Uniform with Delta = default: 222,540 tetrahedra
• Graded with Delta = default: 94,383 tetrahedra

Commands to generate meshes :

Uniform with Delta = default : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Brain-With-Tumor-Case17.nii --output ./Brain-With-Tumor-Case17,d=1.76001.vtk

Graded with Delta = default : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Brain-With-Tumor-Case17.nii --volume-grading --output ./Brain-With-Tumor-Case17,d=1.76001,graded.vtk

Knee-Char

• Input image : Dimensions (512x512x119) with spacing (0.27734x0.27734x1)
• Uniform with Delta = default case: 386,869 tetrahedra
• Graded with Delta = default case: 274,309 tetrahedra

Commands to generate meshes :

Uniform with Delta = default : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Head-Neck.mha --output ./Knee-Char,d=1.19.vtk

Graded with Delta = default : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Head-Neck.mha --volume-grading --output ./Knee-Char,d=1.19,graded.vtk

Ircad2

• Input image : Dimensions (512x512x219) with spacing (0.976562x0.976562x1.40002)
• Uniform with Delta = 2 case: 5,031,442 tetrahedra
• Graded with Delta = 1 case: 6,072,751 tetrahedra

Commands to generate meshes :

Uniform with Delta = 2 : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Ircad2.nrrd --delta 2 --threads 4 --output ./Ircad2,d=2.vtk

Graded with Delta = 1 : Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Ircad2.nrrd --delta 1 --threads 4 --volume-grading --output ./Ircad2,d=1,graded.vtk