Difference between revisions of "Bioinformatics Example Meshes"

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(Created page with "__TOC__ =3D Example Meshes= The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/olefferrnksu2nmerbfvbvsz2u4abbdw Bioninformatics_...")
 
 
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=3D Example Meshes=
 
=3D Example Meshes=
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The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/olefferrnksu2nmerbfvbvsz2u4abbdw Bioninformatics_Data].
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The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/9scj74ix4eulpvgc93sbwte1uvlde8b6 Bioninformatics_Data].
  
 
==COVID-19-Main-Protease-6y2e==
 
==COVID-19-Main-Protease-6y2e==
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Uniform and graded tetrahedral meshes capable to help identify the internal voids of the <b>COVID-19 spike glycoprotein. </b> The middle and right meshes are slices. Biological assembly data was retrieved from the molecule [https://www.rcsb.org/structure/6vxx 6vxx],  [https://www.rcsb.org/ Protein Data Bank].
 
Uniform and graded tetrahedral meshes capable to help identify the internal voids of the <b>COVID-19 spike glycoprotein. </b> The middle and right meshes are slices. Biological assembly data was retrieved from the molecule [https://www.rcsb.org/structure/6vxx 6vxx],  [https://www.rcsb.org/ Protein Data Bank].
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==Brain-With-Tumor-Case17==
 
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* [https://odu.box.com/s/ynudkxeozt5j82v1qmoovif10c73taw7 Input Image]
 
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* Input image : Dimensions (448x512x176) with spacing (0.488281x0.488281x1)
 
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* Uniform with Delta = default: 222,540 tetrahedra
 
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* Graded with Delta = default: 94,383 tetrahedra
 
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<gallery mode="packed" heights=350px>
 
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File:Brain-With-Tumor-Case17,d=1.76001.png
 
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</gallery>
 
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<gallery mode="packed" heights=350px>
 
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File:Brain-With-Tumor-Case17,d=1.76001,graded.png
 
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</gallery>
 
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Commands to generate meshes:
 
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'''Uniform with Delta = default:''' [https://odu.box.com/s/5dqgw5pykve4xv2ii30xnkjq5s3maxdl Output Mesh]
 
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<pre>
 
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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
 
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</pre>
 
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'''Graded with Delta = default:''' [https://odu.box.com/s/8zlfyepfuzmd8g119eup5n2srxpbqg8s Output Mesh]
 
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<pre>
 
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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
 
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</pre>
 
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==Head-Neck==
 
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* [https://odu.box.com/s/zj492g475bo93kyrd1npmisy2gwdqorj Input Image]
 
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* Input image : Dimensions (255x255x229) with spacing (0.976562x0.976562x1.40002)
 
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* Uniform with Delta = default: 205,510 tetrahedra
 
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* Uniform with Delta = 1.5: 767,393 tetrahedra
 
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<gallery mode="packed" heights=350px>
 
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File:Head-Neck,d=2.49023.png
 
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</gallery>
 
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<gallery mode="packed" heights=350px>
 
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File:Head-Neck,d=1.5.png
 
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</gallery>
 
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Commands to generate meshes:
 
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'''Uniform with Delta = default:''' [https://odu.box.com/s/zoi0em9twv0mgt2d1244yujtkd3nit3e Output Mesh]
 
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<pre>
 
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docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Head-Neck.mha --output ./Head-Neck,d=2.49023.vtk
 
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</pre>
 
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'''Uniform with Delta = 1.5:''' [https://odu.box.com/s/jo4cwfrn1bvslocv9b11m0i9dhxyvdrc Output Mesh]
 
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<pre>
 
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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
 
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</pre>
 
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=2D Example Meshes=
 
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The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/olefferrnksu2nmerbfvbvsz2u4abbdw Medical_Imaging_Data].
 
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==COVID-19-23311==
 
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* [https://odu.box.com/s/musv3zdpx6a76p282t2xn905rf0xlwty Input Image]
 
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* Input image : Dimensions (2,460x2,460) with spacing (1x1)
 
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* Uniform with Min-Edge = 20: 47,028 triangles
 
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* Adaptive with Min-Edge = 20: 34,080 tetrahedra
 
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<gallery mode="packed" heights=250px>
 
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File:COVID-19-23311,uniform,e=20,suface.png
 
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File:COVID-19-23311,uniform,e=20,triangulation.png
 
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</gallery>
 
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<gallery mode="packed" heights=250px>
 
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File:COVID-19-23311,w=0.1,e=20,suface.png
 
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File:COVID-19-23311,w=0.1,e=20,triangulation.png
 
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</gallery>
 
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Commands to generate meshes:
 
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'''Uniform with Min-Edge = 20:''' [https://odu.box.com/s/dkv6271ky0cxvk4lio2bvw6jv9zw5iaf Output Mesh]
 
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<pre>
 
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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
 
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</pre>
 
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'''Adaptive with Min-Edge = 20:''' [https://odu.box.com/s/jeqmz2c8dsssl8zujkywl2v3bjaby8gc Output Mesh]
 
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<pre>
 
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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
 
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</pre>
 
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Meshes generated based on the image [https://phil.cdc.gov//PHIL_Images/23311/23311.tif 23311] retrieved from the [https://www.cdc.gov/media/subtopic/images.htm 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.
 
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==COVID-19-23354==
 
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* [https://odu.box.com/s/vwse25rtxemv27sf8vkzofupc4l5mnc7 Input Image]
 
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* Input image : Dimensions (3,000x2,000) with spacing (1x1)
 
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* Uniform with Min-Edge = 15: 82,981 triangles
 
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* Adaptive with Min-Edge = 15: 67,920 triangles
 
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<gallery mode="packed" heights=250px>
 
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File:COVID-19-23354,uniform,e=15,suface.png
 
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File:COVID-19-23354,uniform,e=15,triangulation.png
 
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</gallery>
 
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<gallery mode="packed" heights=250px>
 
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File:COVID-19-23354,w=0.1,e=15,suface.png
 
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File:COVID-19-23354,w=0.1,e=15,triangulation.png
 
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</gallery>
 
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Commands to generate meshes:
 
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'''Uniform with Min-Edge = 15:''' [https://odu.box.com/s/g1ujcqm76q0oxa0cm9dderp9tf5hqubt Output Mesh]
 
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<pre>
 
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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
 
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</pre>
 
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'''Adaptive with Min-Edge = 15:''' [https://odu.box.com/s/bf0w2cfh7ww8wsm45e9yfxahiwb1m864 Output Mesh]
 
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<pre>
 
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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
 
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</pre>
 
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Meshes generated based on the image [https://phil.cdc.gov//PHIL_Images/23354/23354.tif 23354] retrieved from the [https://www.cdc.gov/media/subtopic/images.htm 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.
 

Latest revision as of 22:30, 23 July 2020

3D Example Meshes

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

Retrieved from "https://crtc.cs.odu.edu/index.php?title=Bioinformatics_Example_Meshes&oldid=7117"