Difference between revisions of "Medical Imaging 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 Medical_Imaging_Data].
 
The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/olefferrnksu2nmerbfvbvsz2u4abbdw Medical_Imaging_Data].
  
==COVID-19-Spike-Glycoprotein-6vxx==
+
==Ircad2==
* Input image : Dimensions (281x380x302) with spacing (0.427871x0.427871x0.427871)
+
* [https://odu.box.com/s/1ef11ry4yik5mny62dz6zyd9obqb540i Input Image]
* Uniform with Delta = 0.5: 3,260,055 tetrahedra
+
* 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)
  
<gallery mode="packed" heights=250px>
+
<gallery mode="packed" heights=350px>
File:COVID-19-Spike-Glycoprotein-6vxx,d=5.png
+
File:Ircad2,d=2.png
File:COVID-19-Spike-Glycoprotein-6vxx,d=5,label=1.png
 
File:COVID-19-Spike-Glycoprotein-6vxx,d=5,label=2.png
 
File:COVID-19-Spike-Glycoprotein-6vxx,d=5,label=3.png
 
 
</gallery>
 
</gallery>
 
+
<gallery mode="packed" heights=350px>
Commands to generate meshes:
+
File:Ircad2,d=1,graded.png
 
 
'''Uniform with Delta = 0.5:''' [https://odu.box.com/s/0s3zxk3t2hsvmt43l4i9pabm602tiqbk Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
 
Uniform tetrahedral mesh capable to help identify the internal voids of the <b>COVID-19 spike glycoprotein. </b> Biological assembly data was retrieved from the molecule [https://www.rcsb.org/structure/6vxx 6vxx],  [https://www.rcsb.org/ Protein Data Bank].
 
 
 
==COVID-19-NSP-15-Endoribonuclease-6vww==
 
* Input image : Dimensions (303x297x337) with spacing (0.3445713x0.3445713x0.3445713)
 
* Uniform with Delta = 0.5: 2,310,215 tetrahedra
 
 
 
<gallery mode="packed" heights=250px>
 
File:COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3.png
 
File:COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3,label=1.png
 
File:COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3,label=2.png
 
File:COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3,label=3.png
 
 
</gallery>
 
</gallery>
 
+
<gallery mode="packed" heights=350px>
Commands to generate meshes:
+
File:Ircad2,d=2,el=1.png
 
 
'''Uniform with Delta = 0.5:''' [https://odu.box.com/s/tkk8f8o1tctf52bop07a6hxy87ugt8kg Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
 
Uniform tetrahedral mesh capable to help identify the internal voids of the <b>COVID-19 NSP15 Endoribonuclease. </b> Biological assembly data was retrieved from the molecule [https://www.rcsb.org/structure/6vww 6vww],  [https://www.rcsb.org/ Protein Data Bank].
 
 
 
==COVID-19-Main-Protease-6y2e==
 
* Input image : Dimensions (284x303x344) with spacing (0.2226907x0.2226907x0.2226907)
 
* Uniform with Delta = 0.3: 2,509,202 tetrahedra
 
 
 
<gallery mode="packed" heights=250px>
 
File:COVID-19-Main-Protease-6y2e,d=0.5.png
 
File:COVID-19-Main-Protease-6y2e,d=0.5,label=1.png
 
File:COVID-19-Main-Protease-6y2e,d=0.5,label=2.png
 
 
</gallery>
 
</gallery>
  
 
Commands to generate meshes:
 
Commands to generate meshes:
  
'''Uniform with Delta = 0.3:''' [https://odu.box.com/s/jeks38sn5slw38gbit6t0rf25fzv6jpb Output Mesh]
+
'''Uniform with Delta = 2:''' [https://odu.box.com/s/nfqgcr1j1j6wdnydb9sug8652y9falvc Output Mesh]
 
<pre>
 
<pre>
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
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Ircad2.nrrd --delta 2 --threads 6 --output ./Ircad2,d=2.vtk
 
</pre>
 
</pre>
  
Uniform tetrahedral mesh capable to help identify the internal voids of the <b>COVID-19 Main Protease. </b> Biological assembly data was retrieved from the molecule [https://www.rcsb.org/structure/6y2e 6y2e],  [https://www.rcsb.org/ Protein Data Bank].
+
'''Graded with Delta = 1:''' [https://odu.box.com/s/ofnxslfgijzbqbwqqlvcetm783kqv9uo Output Mesh]
 
 
==COVID-19-Spike-Glycoprotein-6vsb==
 
* 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
 
 
 
<gallery mode="packed" heights=250px>
 
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4.png
 
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,clip.png
 
</gallery>
 
<gallery mode="packed" heights=250px>
 
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded.png
 
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded,clip.png
 
</gallery>
 
 
 
Commands to generate meshes:
 
 
 
'''Uniform with Delta = 0.4:''' [https://odu.box.com/s/wth64bzfotibrwv3t25kn2oyzo0dqntm Output Mesh]
 
 
<pre>
 
<pre>
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
+
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
 
</pre>
 
</pre>
  
'''Graded with Delta = 0.4:''' [https://odu.box.com/s/2kn1ovwpa6jarj8wssmeqz2y5zc19fc2 Output Mesh]
+
'''Uniform with Delta = 2 and excluded label 1:''' [https://odu.box.com/s/5jaukd6317jdmi8li479h8emknuw0rny Output Mesh]
 
<pre>
 
<pre>
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
+
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
 
</pre>
 
</pre>
  
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].
+
==Knee-Char==
 
+
* [https://odu.box.com/s/jv3rqazsth3ktd8ipsstzqjl6ryz8naf Input Image]
==Head-Neck==
+
* Input image : Dimensions (512x512x119) with spacing (0.27734x0.27734x1)
* Input image : Dimensions (255x255x229) with spacing (0.976562x0.976562x1.40002)
+
* Uniform with Delta = default : 386,869 tetrahedra
* Uniform with Delta = default: 205,510 tetrahedra
+
* Graded with Delta = default : 274,309 tetrahedra
* Uniform with Delta = 1.5: 767,393 tetrahedra
 
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
File:Head-Neck,d=2.49023.png
+
File:Knee-Char,d=1.19.png
 
</gallery>
 
</gallery>
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
File:Head-Neck,d=1.5.png
+
File:Knee-Char,d=1.19,graded.png
 
</gallery>
 
</gallery>
  
 
Commands to generate meshes:
 
Commands to generate meshes:
  
'''Uniform with Delta = default:''' [https://odu.box.com/s/zoi0em9twv0mgt2d1244yujtkd3nit3e Output Mesh]
+
'''Uniform with Delta = default :''' [https://odu.box.com/s/q42ekuymr0gy0w882ilcngjfg8l95xvx Output Mesh]
 
<pre>
 
<pre>
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Head-Neck.mha --output ./Head-Neck,d=2.49023.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Knee-Char.mha --output ./Knee-Char,d=1.19.vtk
 
</pre>
 
</pre>
  
'''Uniform with Delta = 1.5:''' [https://odu.box.com/s/jo4cwfrn1bvslocv9b11m0i9dhxyvdrc Output Mesh]
+
'''Graded with Delta = default :''' [https://odu.box.com/s/i788xycxrd11wpxneo4wq3x0ekk9yemm Output Mesh]
 
<pre>
 
<pre>
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
+
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
 
</pre>
 
</pre>
  
 
==Brain-With-Tumor-Case17==
 
==Brain-With-Tumor-Case17==
 +
* [https://odu.box.com/s/ynudkxeozt5j82v1qmoovif10c73taw7 Input Image]
 
* Input image : Dimensions (448x512x176) with spacing (0.488281x0.488281x1)
 
* Input image : Dimensions (448x512x176) with spacing (0.488281x0.488281x1)
 
* Uniform with Delta = default: 222,540 tetrahedra
 
* Uniform with Delta = default: 222,540 tetrahedra
Line 139: Line 88:
 
</pre>
 
</pre>
  
==Knee-Char==
+
==Head-Neck==
* Input image : Dimensions (512x512x119) with spacing (0.27734x0.27734x1)
+
* [https://odu.box.com/s/zj492g475bo93kyrd1npmisy2gwdqorj Input Image]
* Uniform with Delta = default : 386,869 tetrahedra
+
* Input image : Dimensions (255x255x229) with spacing (0.976562x0.976562x1.40002)
* Graded with Delta = default : 274,309 tetrahedra
+
* Uniform with Delta = default: 205,510 tetrahedra
 +
* Uniform with Delta = 1.5: 767,393 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
File:Knee-Char,d=1.19.png
+
File:Head-Neck,d=2.49023.png
 
</gallery>
 
</gallery>
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
File:Knee-Char,d=1.19,graded.png
+
File:Head-Neck,d=1.5.png
 
</gallery>
 
</gallery>
  
 
Commands to generate meshes:
 
Commands to generate meshes:
  
'''Uniform with Delta = default :''' [https://odu.box.com/s/q42ekuymr0gy0w882ilcngjfg8l95xvx Output Mesh]
+
'''Uniform with Delta = default:''' [https://odu.box.com/s/zoi0em9twv0mgt2d1244yujtkd3nit3e Output Mesh]
 
<pre>
 
<pre>
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Knee-Char.mha --output ./Knee-Char,d=1.19.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Head-Neck.mha --output ./Head-Neck,d=2.49023.vtk
 
</pre>
 
</pre>
  
'''Graded with Delta = default :''' [https://odu.box.com/s/i788xycxrd11wpxneo4wq3x0ekk9yemm Output Mesh]
+
'''Uniform with Delta = 1.5:''' [https://odu.box.com/s/jo4cwfrn1bvslocv9b11m0i9dhxyvdrc Output Mesh]
 
<pre>
 
<pre>
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
+
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
</pre>
 
 
 
==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)
 
 
 
<gallery mode="packed" heights=350px>
 
File:Ircad2,d=2.png
 
</gallery>
 
<gallery mode="packed" heights=350px>
 
File:Ircad2,d=1,graded.png
 
</gallery>
 
Commands to generate meshes:
 
 
 
'''Uniform with Delta = 2:''' [https://odu.box.com/s/nfqgcr1j1j6wdnydb9sug8652y9falvc Output Mesh]
 
<pre>
 
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Ircad2.nrrd --delta 2 --threads 6 --output ./Ircad2,d=2.vtk
 
 
</pre>
 
</pre>
 
'''Graded with Delta = 1:''' [https://odu.box.com/s/ofnxslfgijzbqbwqqlvcetm783kqv9uo Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
=2D Example Meshes=
 
The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/olefferrnksu2nmerbfvbvsz2u4abbdw 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
 
 
<gallery mode="packed" heights=250px>
 
File:COVID-19-23354,uniform,e=15,suface.png
 
File:COVID-19-23354,uniform,e=15,triangulation.png
 
</gallery>
 
<gallery mode="packed" heights=250px>
 
File:COVID-19-23354,w=0.1,e=15,suface.png
 
File:COVID-19-23354,w=0.1,e=15,triangulation.png
 
</gallery>
 
 
Commands to generate meshes:
 
 
'''Uniform with Min-Edge = 15:''' [https://odu.box.com/s/g1ujcqm76q0oxa0cm9dderp9tf5hqubt Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
'''Adaptive with Min-Edge = 15:''' [https://odu.box.com/s/bf0w2cfh7ww8wsm45e9yfxahiwb1m864 Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
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.
 
 
==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
 
 
<gallery mode="packed" heights=250px>
 
File:COVID-19-23311,uniform,e=20,suface.png
 
File:COVID-19-23311,uniform,e=20,triangulation.png
 
</gallery>
 
<gallery mode="packed" heights=250px>
 
File:COVID-19-23311,w=0.1,e=20,suface.png
 
File:COVID-19-23311,w=0.1,e=20,triangulation.png
 
</gallery>
 
 
Commands to generate meshes:
 
 
'''Uniform with Min-Edge = 20:''' [https://odu.box.com/s/dkv6271ky0cxvk4lio2bvw6jv9zw5iaf Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
'''Adaptive with Min-Edge = 20:''' [https://odu.box.com/s/jeqmz2c8dsssl8zujkywl2v3bjaby8gc Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
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.
 

Latest revision as of 11:57, 2 June 2020

3D Example Meshes

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

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)

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

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

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

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

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