Revision as of 00:58, 16 March 2020

2D Example Meshes

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

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 Centers for Disease Control and Prevention. 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

Mesh generated based on the images published at Centers for Disease Control and Prevention. For the uniform meshes, the edge-size corresponds to 20 pixels. The adaptive meshes were created by controlling the size of the elements based on the difference in the intensity of the pixels.

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: 205,510 tetrahedra
• Uniform with Delta = 1.5: 767,393 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 : 386,869 tetrahedra
• Graded with Delta = default : 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/Knee-Char.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/Knee-Char.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: 5,031,442 tetrahedra (#elements for each execution might differ due to the usage of more than one thread)
• Graded with Delta = 1: 6,072,751 tetrahedra (#elements for each execution might differ due to the usage of more than one thread)

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 6 --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 6 --volume-grading --output ./Ircad2,d=1,graded.vtk

COVID-19-Spike-Glycoprotein-6vsb

• Input image : Dimensions (220x223x314) with spacing (0.551042x0.551042x0.551042)
• Uniform with Delta = 0.5: 5,731,833 tetrahedra
• Graded with Delta = 0.4: 3,794,223 tetrahedra

[[File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4.png|500px|COVID-19-Spike-Glycoprotein-6vsb,d=0.4.png] [[File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,clip.png|500px|COVID-19-Spike-Glycoprotein-6vsb,d=0.4,clip.png] [[File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,clip,zoom.png|500px|COVID-19-Spike-Glycoprotein-6vsb,d=0.4,clip,zoom.png]

[[File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded.png|500px|COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded.png] [[File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded,clip.png|500px|COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded,clip.png] [[File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded,clip,zoom.png|500px|COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded,clip,zoom.png]

Commands to generate meshes :

Uniform with Delta = 0.4: [ Output Mesh]

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/COVID-19-Spike-Glycoprotein-6vsb.nrrd --delta 0.4 --output ./COVID-19-Spike-Glycoprotein-6vsb,d=0.4.vtk

Graded with Delta = 0.4: Output Mesh

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/COVID-19-Spike-Glycoprotein-6vsb.nrrd --delta 0.4 --volume-grading --output ./COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded.vtk