Difference between revisions of "Medical Imaging Example Meshes"

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(COVID-19-Main-Protease-6y2e)
(COVID-19-NSP-15-Endoribonuclease-6vww)
 
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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==
+
==COVID-19-Main-Protease-6y2e==
* Input image : Dimensions (281x380x302) with spacing (0.427871x0.427871x0.427871)
+
* [https://odu.box.com/s/eb9s3vrvml0gop7mdpwckh6xnn27eiut Input Image]
* Uniform with Delta = 0.5: 3,260,055 tetrahedra
+
* Input image : Dimensions (284x303x344) with spacing (0.2226907x0.2226907x0.2226907)
 +
* Uniform with Delta = 0.3: 2,509,202 tetrahedra
  
 
<gallery mode="packed" heights=250px>
 
<gallery mode="packed" heights=250px>
File:COVID-19-Spike-Glycoprotein-6vxx,d=5.png
+
File:COVID-19-Main-Protease-6y2e,d=0.5.png
File:COVID-19-Spike-Glycoprotein-6vxx,d=5,label=1.png
+
File:COVID-19-Main-Protease-6y2e,d=0.5,clip.png
File:COVID-19-Spike-Glycoprotein-6vxx,d=5,label=2.png
+
</gallery>
File:COVID-19-Spike-Glycoprotein-6vxx,d=5,label=3.png
+
<gallery mode="packed" heights=250px>
 +
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 = 0.5:''' [https://odu.box.com/s/0s3zxk3t2hsvmt43l4i9pabm602tiqbk Output Mesh]
+
'''Uniform with Delta = 0.3:''' [https://odu.box.com/s/jeks38sn5slw38gbit6t0rf25fzv6jpb Output Mesh]
 
<pre>
 
<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
+
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
 
</pre>
 
</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].
+
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].
  
 
==COVID-19-NSP-15-Endoribonuclease-6vww==
 
==COVID-19-NSP-15-Endoribonuclease-6vww==
 +
* [Input Image]
 
* Input image : Dimensions (303x297x337) with spacing (0.3445713x0.3445713x0.3445713)
 
* Input image : Dimensions (303x297x337) with spacing (0.3445713x0.3445713x0.3445713)
* Uniform with Delta = 0.3: 2,310,215 tetrahedra
+
* Uniform with Delta = 0.5: 2,310,215 tetrahedra
  
 
<gallery mode="packed" heights=250px>
 
<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.png
 +
File:COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3,clip.png
 +
</gallery>
 +
<gallery mode="packed" heights=250px>
 
File:COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3,label=1.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=2.png
Line 37: Line 44:
 
Commands to generate meshes:
 
Commands to generate meshes:
  
'''Uniform with Delta = 0.3:''' [https://odu.box.com/s/tkk8f8o1tctf52bop07a6hxy87ugt8kg Output Mesh]
+
'''Uniform with Delta = 0.5:''' [https://odu.box.com/s/tkk8f8o1tctf52bop07a6hxy87ugt8kg Output Mesh]
 
<pre>
 
<pre>
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/COVID-19-NSP-15-Endoribonuclease-6vww.nrrd --delta 0.3 --output ./COVID-19-NSP-15-Endoribonuclease-6vww,d=0.3.vtk
+
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>
 
</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].
 
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==
+
==COVID-19-Spike-Glycoprotein-6vxx==
* Input image : Dimensions (284x303x344) with spacing (0.2226907x0.2226907x0.2226907)
+
* [https://odu.box.com/s/jt2z4vqq3vs8211uuah7m2qfnyk1tjq8 Input Image]
* Uniform with Delta = 0.3: 2,509,202 tetrahedra
+
* Input image : Dimensions (281x380x302) with spacing (0.427871x0.427871x0.427871)
 +
* Uniform with Delta = 0.5: 3,260,055 tetrahedra
 +
 
 +
<gallery mode="packed" heights=250px>
 +
File:COVID-19-Spike-Glycoprotein-6vxx,d=5.png
 +
File:COVID-19-Spike-Glycoprotein-6vxx,d=5,zoom.png
 +
</gallery>
  
 
<gallery mode="packed" heights=250px>
 
<gallery mode="packed" heights=250px>
File:COVID-19-Main-Protease-6y2e,d=0.5.png
+
File:COVID-19-Spike-Glycoprotein-6vxx,d=5,label=1.png
File:COVID-19-Main-Protease-6y2e,d=0.5,label=1.png
+
File:COVID-19-Spike-Glycoprotein-6vxx,d=5,label=2.png
File:COVID-19-Main-Protease-6y2e,d=0.5,label=2.png
+
File:COVID-19-Spike-Glycoprotein-6vxx,d=5,label=3.png
 
</gallery>
 
</gallery>
  
 
Commands to generate meshes:
 
Commands to generate meshes:
  
'''Uniform with Delta = 0.5:''' [https://odu.box.com/s/jeks38sn5slw38gbit6t0rf25fzv6jpb Output Mesh]
+
'''Uniform with Delta = 0.5:''' [https://odu.box.com/s/0s3zxk3t2hsvmt43l4i9pabm602tiqbk 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/COVID-19-Spike-Glycoprotein-6vxx.nrrd --delta 0.5 --output ./COVID-19-Spike-Glycoprotein-6vxx,d=5.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].
+
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-Spike-Glycoprotein-6vsb==
 
==COVID-19-Spike-Glycoprotein-6vsb==
 +
* [https://odu.box.com/s/hebeasclyuekcavgvftb9vbsirrbqj7t Input Image]
 
* Input image : Dimensions (220x223x314) with spacing (0.551042x0.551042x0.551042)
 
* Input image : Dimensions (220x223x314) with spacing (0.551042x0.551042x0.551042)
 
* Uniform with Delta = 0.4: 5,731,833 tetrahedra
 
* Uniform with Delta = 0.4: 5,731,833 tetrahedra
Line 90: Line 104:
  
 
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].
 
==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
 
 
<gallery mode="packed" heights=350px>
 
File:Head-Neck,d=2.49023.png
 
</gallery>
 
<gallery mode="packed" heights=350px>
 
File:Head-Neck,d=1.5.png
 
</gallery>
 
 
Commands to generate meshes:
 
 
'''Uniform with Delta = default:''' [https://odu.box.com/s/zoi0em9twv0mgt2d1244yujtkd3nit3e Output Mesh]
 
<pre>
 
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./Medical_Imaging_Data/3D/Head-Neck.mha --output ./Head-Neck,d=2.49023.vtk
 
</pre>
 
 
'''Uniform with Delta = 1.5:''' [https://odu.box.com/s/jo4cwfrn1bvslocv9b11m0i9dhxyvdrc Output Mesh]
 
<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
 
</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 130:
 
</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>
 
</pre>
  
 
==Ircad2==
 
==Ircad2==
 +
* [https://odu.box.com/s/1ef11ry4yik5mny62dz6zyd9obqb540i Input Image]
 
* Input image : Dimensions (512x512x219) with spacing (0.976562x0.976562x1.40002)
 
* 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)
 
* 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)
 
* 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=350px>
 
<gallery mode="packed" heights=350px>
Line 174: Line 168:
 
File:Ircad2,d=1,graded.png
 
File:Ircad2,d=1,graded.png
 
</gallery>
 
</gallery>
 +
<gallery mode="packed" heights=350px>
 +
File:Ircad2,d=2,el=1.png
 +
</gallery>
 +
 
Commands to generate meshes:
 
Commands to generate meshes:
  
Line 186: Line 184:
 
</pre>
 
</pre>
  
=2D Example Meshes=
+
'''Uniform with Delta = 2 and excluded label 1:''' [https://odu.box.com/s/5jaukd6317jdmi8li479h8emknuw0rny Output Mesh]
The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/olefferrnksu2nmerbfvbvsz2u4abbdw Medical_Imaging_Data].
+
<pre>
 +
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>
  
==COVID-19-23354==
+
==Knee-Char==
* Input image : Dimensions (3,000x2,000) with spacing (1x1)
+
* [https://odu.box.com/s/jv3rqazsth3ktd8ipsstzqjl6ryz8naf Input Image]
* Uniform with Min-Edge = 15: 82,981 triangles
+
* Input image : Dimensions (512x512x119) with spacing (0.27734x0.27734x1)
* Adaptive with Min-Edge = 15: 67,920 triangles
+
* Uniform with Delta = default : 386,869 tetrahedra
 +
* Graded with Delta = default : 274,309 tetrahedra
  
<gallery mode="packed" heights=250px>
+
<gallery mode="packed" heights=350px>
File:COVID-19-23354,uniform,e=15,suface.png
+
File:Knee-Char,d=1.19.png
File:COVID-19-23354,uniform,e=15,triangulation.png
 
 
</gallery>
 
</gallery>
<gallery mode="packed" heights=250px>
+
<gallery mode="packed" heights=350px>
File:COVID-19-23354,w=0.1,e=15,suface.png
+
File:Knee-Char,d=1.19,graded.png
File:COVID-19-23354,w=0.1,e=15,triangulation.png
 
 
</gallery>
 
</gallery>
  
 
Commands to generate meshes:
 
Commands to generate meshes:
  
'''Uniform with Min-Edge = 15:''' [https://odu.box.com/s/g1ujcqm76q0oxa0cm9dderp9tf5hqubt Output Mesh]
+
'''Uniform with Delta = default :''' [https://odu.box.com/s/q42ekuymr0gy0w882ilcngjfg8l95xvx Output Mesh]
 
<pre>
 
<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
+
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>
  
'''Adaptive with Min-Edge = 15:''' [https://odu.box.com/s/bf0w2cfh7ww8wsm45e9yfxahiwb1m864 Output Mesh]
+
'''Graded with Delta = default :''' [https://odu.box.com/s/i788xycxrd11wpxneo4wq3x0ekk9yemm Output Mesh]
 
<pre>
 
<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
+
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>
  
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.
+
=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-19-23311==
 
==COVID-19-23311==
 +
* [https://odu.box.com/s/musv3zdpx6a76p282t2xn905rf0xlwty Input Image]
 
* Input image : Dimensions (2,460x2,460) with spacing (1x1)
 
* Input image : Dimensions (2,460x2,460) with spacing (1x1)
 
* Uniform with Min-Edge = 20: 47,028 triangles
 
* Uniform with Min-Edge = 20: 47,028 triangles
Line 244: Line 245:
  
 
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.
 
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.
 +
 +
==COVID-19-23354==
 +
* [https://odu.box.com/s/vwse25rtxemv27sf8vkzofupc4l5mnc7 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
 +
 +
<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.

Latest revision as of 12:01, 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

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

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

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

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

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

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

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

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

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.