Difference between revisions of "Medical Imaging Example Meshes"

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(COVID-19-Spike-Glycoprotein-6vxx)
(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 () with spacing ()
+
* [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 : Dimensions () with spacing ()
+
* [Input Image]
* Uniform with Delta = 0.3: 2,310,215 tetrahedra
+
* Input image : Dimensions (303x297x337) with spacing (0.3445713x0.3445713x0.3445713)
 +
* 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 () with spacing ()
+
* [https://odu.box.com/s/jt2z4vqq3vs8211uuah7m2qfnyk1tjq8 Input Image]
* Uniform with Delta = 0.5: 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 0.5 --output ./COVID-19-Main-Protease-6y2e,d=0.5.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 71: Line 85:
 
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4.png
 
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4.png
 
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,clip.png
 
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,clip.png
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,clip,zoom.png
 
 
</gallery>
 
</gallery>
 
<gallery mode="packed" heights=250px>
 
<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.png
 
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded,clip.png
 
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded,clip.png
File:COVID-19-Spike-Glycoprotein-6vsb,d=0.4,graded,clip,zoom.png
 
 
</gallery>
 
</gallery>
  
Line 92: 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
 
File:Head-Neck,d=2.49023,zoom.png
 
</gallery>
 
<gallery mode="packed" heights=350px>
 
File:Head-Neck,d=1.5.png
 
File:Head-Neck,d=1.5,zoom.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 126: Line 113:
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
 
File:Brain-With-Tumor-Case17,d=1.76001.png
 
File:Brain-With-Tumor-Case17,d=1.76001.png
File:Brain-With-Tumor-Case17,d=1.76001,zoom.png
 
 
</gallery>
 
</gallery>
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
 
File:Brain-With-Tumor-Case17,d=1.76001,graded.png
 
File:Brain-With-Tumor-Case17,d=1.76001,graded.png
File:Brain-With-Tumor-Case17,d=1.76001,graded,zoom.png
 
 
</gallery>
 
</gallery>
  
Line 145: 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
File:Knee-Char,d=1.19,zoom.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
File:Knee-Char,d=1.19,graded,zoom.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>
 
File:Ircad2,d=2.png
 
File:Ircad2,d=2.png
File:Ircad2,d=2,zoom.png
 
 
</gallery>
 
</gallery>
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
 
File:Ircad2,d=1,graded.png
 
File:Ircad2,d=1,graded.png
File:Ircad2,d=1,graded,zoom.png
+
</gallery>
 +
<gallery mode="packed" heights=350px>
 +
File:Ircad2,d=2,el=1.png
 
</gallery>
 
</gallery>
  
Line 197: 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
 
File:COVID-19-23354,uniform,e=15,triangulation,zoom.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
 
File:COVID-19-23354,w=0.1,e=15,triangulation,zoom.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>
  
Mesh 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 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.
+
=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 238: Line 226:
 
File:COVID-19-23311,uniform,e=20,suface.png
 
File:COVID-19-23311,uniform,e=20,suface.png
 
File:COVID-19-23311,uniform,e=20,triangulation.png
 
File:COVID-19-23311,uniform,e=20,triangulation.png
File:COVID-19-23311,uniform,e=20,triangulation,zoom.png
 
 
</gallery>
 
</gallery>
 
<gallery mode="packed" heights=250px>
 
<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,suface.png
 
File:COVID-19-23311,w=0.1,e=20,triangulation.png
 
File:COVID-19-23311,w=0.1,e=20,triangulation.png
File:COVID-19-23311,w=0.1,e=20,triangulation,zoom.png
 
 
</gallery>
 
</gallery>
  
Line 258: Line 244:
 
</pre>
 
</pre>
  
Mesh 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 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.
+
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.

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

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.