Difference between revisions of "Medical Imaging Example Meshes"

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Revision as of 16:01, 20 April 2020

3D Example Meshes

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

COVID-19-Main-Protease-6y2e

  • Input Image
  • Input image : Dimensions (284x303x344) with spacing (0.2226907x0.2226907x0.2226907)
  • Uniform with Delta = 0.3: 2,509,202 tetrahedra
  • COVID-19-Main-Protease-6y2e,d=0.5.png
  • COVID-19-Main-Protease-6y2e,d=0.5,clip.png
  • COVID-19-Main-Protease-6y2e,d=0.5,label=1.png
  • COVID-19-Main-Protease-6y2e,d=0.5,label=2.png

Commands to generate meshes:

Uniform with Delta = 0.3: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/COVID-19-Main-Protease-6y2e.nrrd --delta 0.3 --output ./COVID-19-Main-Protease-6y2e,d=0.3.vtk

Uniform tetrahedral mesh capable to help identify the internal voids of the COVID-19 Main Protease. Biological assembly data was retrieved from the molecule 6y2e, Protein Data Bank.

COVID-19-NSP-15-Endoribonuclease-6vww

  • [Input Image]
  • Input image : Dimensions (303x297x337) with spacing (0.3445713x0.3445713x0.3445713)
  • Uniform with Delta = 0.5: 2,310,215 tetrahedra
  • COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3.png
  • COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3,clip.png
  • COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3.png
  • COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3,label=1.png
  • COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3,label=2.png
  • COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3,label=3.png

Commands to generate meshes:

Uniform with Delta = 0.5: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/COVID-19-NSP-15-Endoribonuclease-6vww.nrrd --delta 0.5 --output ./COVID-19-NSP-15-Endoribonuclease-6vww,d=0.5.vtk

Uniform tetrahedral mesh capable to help identify the internal voids of the COVID-19 NSP15 Endoribonuclease. Biological assembly data was retrieved from the molecule 6vww, Protein Data Bank.

COVID-19-Spike-Glycoprotein-6vxx

  • Input Image
  • Input image : Dimensions (281x380x302) with spacing (0.427871x0.427871x0.427871)
  • Uniform with Delta = 0.5: 3,260,055 tetrahedra
  • COVID-19-Spike-Glycoprotein-6vxx,d=5.png
  • COVID-19-Spike-Glycoprotein-6vxx,d=5,zoom.png
  • COVID-19-Spike-Glycoprotein-6vxx,d=5,label=1.png
  • COVID-19-Spike-Glycoprotein-6vxx,d=5,label=2.png
  • COVID-19-Spike-Glycoprotein-6vxx,d=5,label=3.png

Commands to generate meshes:

Uniform with Delta = 0.5: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/COVID-19-Spike-Glycoprotein-6vxx.nrrd --delta 0.5 --output ./COVID-19-Spike-Glycoprotein-6vxx,d=5.vtk

Uniform tetrahedral mesh capable to help identify the internal voids of the COVID-19 spike glycoprotein. Biological assembly data was retrieved from the molecule 6vxx, Protein Data Bank.

COVID-19-Spike-Glycoprotein-6vsb

  • Input Image
  • Input image : Dimensions (220x223x314) with spacing (0.551042x0.551042x0.551042)
  • Uniform with Delta = 0.4: 5,731,833 tetrahedra
  • Graded with Delta = 0.4: 3,794,223 tetrahedra
  • COVID-19-Spike-Glycoprotein-6vsb,d=0.4.png
  • COVID-19-Spike-Glycoprotein-6vsb,d=0.4,clip.png
  • COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded.png
  • COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded,clip.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

Uniform and graded tetrahedral meshes capable to help identify the internal voids of the COVID-19 spike glycoprotein. The middle and right meshes are slices. Biological assembly data was retrieved from the molecule 6vxx, Protein Data Bank.

Brain-With-Tumor-Case17

  • Input Image
  • Input image : Dimensions (448x512x176) with spacing (0.488281x0.488281x1)
  • Uniform with Delta = default: 222,540 tetrahedra
  • Graded with Delta = default: 94,383 tetrahedra
  • Brain-With-Tumor-Case17,d=1.76001.png
  • Brain-With-Tumor-Case17,d=1.76001,graded.png

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

Head-Neck

  • Input Image
  • 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
  • Head-Neck,d=2.49023.png
  • Head-Neck,d=1.5.png

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

Ircad2

  • Input Image
  • 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)
  • Uniform with Delta = 2 and excluded label 1: 1,941,468 tetrahedra (#elements for each execution might differ due to the usage of more than one thread)
  • Ircad2,d=2.png
  • Ircad2,d=1,graded.png
  • Ircad2,d=2,el=1.png

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

Uniform with Delta = 2 and excluded label 1: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Ircad2.nrrd --exclude-labels 1 --delta 2 --threads 6 --output ./Ircad2,d=2,el=1.vtk

Knee-Char

  • Input Image
  • Input image : Dimensions (512x512x119) with spacing (0.27734x0.27734x1)
  • Uniform with Delta = default : 386,869 tetrahedra
  • Graded with Delta = default : 274,309 tetrahedra
  • Knee-Char,d=1.19.png
  • Knee-Char,d=1.19,graded.png

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

2D Example Meshes

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

COVID-19-23311

  • Input Image
  • 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
  • COVID-19-23311,uniform,e=20,suface.png
  • COVID-19-23311,uniform,e=20,triangulation.png
  • COVID-19-23311,w=0.1,e=20,suface.png
  • COVID-19-23311,w=0.1,e=20,triangulation.png

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

Meshes generated based on the image 23311 retrieved from the Centers for Disease Control and Prevention. For the uniform mesh, the edge-size corresponds to 20 pixels. The adaptive was created by controlling the size of the elements based on the difference in the intensity of the pixels.

COVID-19-23354

  • Input Image
  • 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
  • COVID-19-23354,uniform,e=15,suface.png
  • COVID-19-23354,uniform,e=15,triangulation.png
  • COVID-19-23354,w=0.1,e=15,suface.png
  • COVID-19-23354,w=0.1,e=15,triangulation.png

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

Meshes generated based on the image 23354 retrieved from the Centers for Disease Control and Prevention. For the uniform mesh, the edge-size corresponds to 15 pixels. The adaptive was created by controlling the size of the elements based on the difference in the intensity of the pixels.

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