Difference between revisions of "Medical Imaging Example Meshes"

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(→‎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 = 0.5: 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].
 +
 +
==Brain-With-Tumor-Case17==
 +
* [https://odu.box.com/s/ynudkxeozt5j82v1qmoovif10c73taw7 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
 +
 +
<gallery mode="packed" heights=350px>
 +
File:Brain-With-Tumor-Case17,d=1.76001.png
 +
</gallery>
 +
<gallery mode="packed" heights=350px>
 +
File:Brain-With-Tumor-Case17,d=1.76001,graded.png
 +
</gallery>
 +
 +
Commands to generate meshes:
 +
 +
'''Uniform with Delta = default:''' [https://odu.box.com/s/5dqgw5pykve4xv2ii30xnkjq5s3maxdl Output Mesh]
 +
<pre>
 +
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
 +
</pre>
 +
 +
'''Graded with Delta = default:''' [https://odu.box.com/s/8zlfyepfuzmd8g119eup5n2srxpbqg8s Output Mesh]
 +
<pre>
 +
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
 +
</pre>
  
 
==Head-Neck==
 
==Head-Neck==
 +
* [https://odu.box.com/s/zj492g475bo93kyrd1npmisy2gwdqorj Input Image]
 
* Input image : Dimensions (255x255x229) with spacing (0.976562x0.976562x1.40002)
 
* Input image : Dimensions (255x255x229) with spacing (0.976562x0.976562x1.40002)
 
* Uniform with Delta = default: 205,510 tetrahedra
 
* Uniform with Delta = default: 205,510 tetrahedra
Line 100: Line 138:
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
 
File:Head-Neck,d=2.49023.png
 
File:Head-Neck,d=2.49023.png
−
File:Head-Neck,d=2.49023,zoom.png
 
 
</gallery>
 
</gallery>
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
 
File:Head-Neck,d=1.5.png
 
File:Head-Neck,d=1.5.png
−
File:Head-Neck,d=1.5,zoom.png
 
 
</gallery>
 
</gallery>
  
Line 119: Line 155:
 
</pre>
 
</pre>
  
−
==Brain-With-Tumor-Case17==
+
==Ircad2==
−
* Input image : Dimensions (448x512x176) with spacing (0.488281x0.488281x1)
+
* [https://odu.box.com/s/1ef11ry4yik5mny62dz6zyd9obqb540i Input Image]
−
* Uniform with Delta = default: 222,540 tetrahedra
+
* Input image : Dimensions (512x512x219) with spacing (0.976562x0.976562x1.40002)
−
* Graded with Delta = default: 94,383 tetrahedra
+
* 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=350px>
 
<gallery mode="packed" heights=350px>
−
File:Brain-With-Tumor-Case17,d=1.76001.png
+
File:Ircad2,d=2.png
−
File:Brain-With-Tumor-Case17,d=1.76001,zoom.png
+
</gallery>
 +
<gallery mode="packed" heights=350px>
 +
File:Ircad2,d=1,graded.png
 
</gallery>
 
</gallery>
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
−
File:Brain-With-Tumor-Case17,d=1.76001,graded.png
+
File:Ircad2,d=2,el=1.png
−
File:Brain-With-Tumor-Case17,d=1.76001,graded,zoom.png
 
 
</gallery>
 
</gallery>
  
 
Commands to generate meshes:
 
Commands to generate meshes:
  
−
'''Uniform with Delta = default:''' [https://odu.box.com/s/5dqgw5pykve4xv2ii30xnkjq5s3maxdl Output Mesh]
+
'''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>
 +
 
 +
'''Graded with Delta = 1:''' [https://odu.box.com/s/ofnxslfgijzbqbwqqlvcetm783kqv9uo Output Mesh]
 
<pre>
 
<pre>
−
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
+
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 = default:''' [https://odu.box.com/s/8zlfyepfuzmd8g119eup5n2srxpbqg8s 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/Brain-With-Tumor-Case17.nii --volume-grading --output ./Brain-With-Tumor-Case17,d=1.76001,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>
  
 
==Knee-Char==
 
==Knee-Char==
 +
* [https://odu.box.com/s/jv3rqazsth3ktd8ipsstzqjl6ryz8naf Input Image]
 
* Input image : Dimensions (512x512x119) with spacing (0.27734x0.27734x1)
 
* Input image : Dimensions (512x512x119) with spacing (0.27734x0.27734x1)
 
* Uniform with Delta = default : 386,869 tetrahedra
 
* Uniform with Delta = default : 386,869 tetrahedra
Line 152: Line 197:
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
 
File:Knee-Char,d=1.19.png
 
File:Knee-Char,d=1.19.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:Knee-Char,d=1.19,graded.png
−
File:Knee-Char,d=1.19,graded,zoom.png
 
 
</gallery>
 
</gallery>
  
Line 171: Line 214:
 
</pre>
 
</pre>
  
−
==Ircad2==
+
=2D Example Meshes=
−
* Input image : Dimensions (512x512x219) with spacing (0.976562x0.976562x1.40002)
+
The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/olefferrnksu2nmerbfvbvsz2u4abbdw Medical_Imaging_Data].
−
* 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)
+
==COVID-19-23311==
 +
* [https://odu.box.com/s/musv3zdpx6a76p282t2xn905rf0xlwty 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
  
−
File:Ircad2,d=2.png
+
<gallery mode="packed" heights=250px>
−
File:Ircad2,d=1,graded.png
+
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:
 
Commands to generate meshes:
  
−
'''Uniform with Delta = 2:''' [https://odu.box.com/s/nfqgcr1j1j6wdnydb9sug8652y9falvc Output Mesh]
+
'''Uniform with Min-Edge = 20:''' [https://odu.box.com/s/dkv6271ky0cxvk4lio2bvw6jv9zw5iaf Output Mesh]
 
<pre>
 
<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
+
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>
 
</pre>
  
−
'''Graded with Delta = 1:''' [https://odu.box.com/s/ofnxslfgijzbqbwqqlvcetm783kqv9uo Output Mesh]
+
'''Adaptive with Min-Edge = 20:''' [https://odu.box.com/s/jeqmz2c8dsssl8zujkywl2v3bjaby8gc Output Mesh]
 
<pre>
 
<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
+
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>
 
</pre>
  
−
=2D Example Meshes=
+
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.
−
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==
 
==COVID-19-23354==
 +
* [https://odu.box.com/s/vwse25rtxemv27sf8vkzofupc4l5mnc7 Input Image]
 
* Input image : Dimensions (3,000x2,000) with spacing (1x1)
 
* Input image : Dimensions (3,000x2,000) with spacing (1x1)
 
* Uniform with Min-Edge = 15: 82,981 triangles
 
* Uniform with Min-Edge = 15: 82,981 triangles
Line 220: Line 273:
 
</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.
+
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.
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==COVID-19-23311==
 
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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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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.
 

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,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.

Retrieved from "https://crtc.cs.odu.edu/index.php?title=Medical_Imaging_Example_Meshes&oldid=5640"