Difference between revisions of "ITK Analysis of Large Histology Datasets"
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| + | [[File: pipe.JPG]] | ||
| + | [[File: reg.JPG]] | ||
| + | [[File: seg.JPG]] | ||
| + | ==Key Investigators== | ||
| + | * OSU: Liya Ding, Kun Huang, Raghu Machiraju | ||
| + | * Harvard Medical School: Sean Megason | ||
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==Project== | ==Project== | ||
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<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
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<h3>Objective</h3> | <h3>Objective</h3> | ||
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3D histology stacks are being increasingly used to understand gross anatomical changes and to provide | 3D histology stacks are being increasingly used to understand gross anatomical changes and to provide | ||
valuable educational contexts. Most existing toolkits allow a 2D approach and do not meet the challenges | valuable educational contexts. Most existing toolkits allow a 2D approach and do not meet the challenges | ||
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sensible registration, segmentation and reconstruction of digital slides depicting various organs and tissue | sensible registration, segmentation and reconstruction of digital slides depicting various organs and tissue | ||
systems. Modules will be included that will allow for pre-processing (color correction, artifact removal, | systems. Modules will be included that will allow for pre-processing (color correction, artifact removal, | ||
| − | etc.), rigid and non-rigid registration, material-based segmentation, and visualization. | + | etc.), rigid and non-rigid registration, material-based segmentation, and visualization. |
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</div> | </div> | ||
| − | <div style="width: | + | <div style="width: 27%; float: left; padding-right: 3%;"> |
| + | <h3>Approach, Plan</h3> | ||
we have developed a series of algorithms and | we have developed a series of algorithms and | ||
computational pipelines for processing large microscopy images using heterogeneous | computational pipelines for processing large microscopy images using heterogeneous | ||
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microscopy images to enable both 2D and 3D digital histology. | microscopy images to enable both 2D and 3D digital histology. | ||
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<h3>Progress</h3> | <h3>Progress</h3> | ||
| + | We are creating three different workflows to achieve our goals. These | ||
| + | workflows accomplish (i) pre-preprocessing the data, (ii) registration/3D reconstruction | ||
| + | and segmentation/classification of tissue regions from multi-channel data, (iii) and the | ||
| + | visualization of the microstructure. | ||
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| + | During this project week, we learn about ITK modules for registration, ITK filters and 3D Slicer. | ||
| + | We will convert our algorithms into ITK modules and also into a pipeline in 3D slicer in the future. | ||
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Latest revision as of 14:28, 25 June 2010
Home < ITK Analysis of Large Histology Datasets
Key Investigators
- OSU: Liya Ding, Kun Huang, Raghu Machiraju
- Harvard Medical School: Sean Megason
Project
Objective
3D histology stacks are being increasingly used to understand gross anatomical changes and to provide valuable educational contexts. Most existing toolkits allow a 2D approach and do not meet the challenges posed by 3D histology. ITKv4 can facilitate the realization application level toolkits that will allow for a sensible registration, segmentation and reconstruction of digital slides depicting various organs and tissue systems. Modules will be included that will allow for pre-processing (color correction, artifact removal, etc.), rigid and non-rigid registration, material-based segmentation, and visualization.
Approach, Plan
we have developed a series of algorithms and computational pipelines for processing large microscopy images using heterogeneous computing platforms including GPU and CPU/GPU clusters. We will extend ITKv4 by incorporating algorithm families that will allow for comprehensive processing of light microscopy images to enable both 2D and 3D digital histology.
Progress
We are creating three different workflows to achieve our goals. These workflows accomplish (i) pre-preprocessing the data, (ii) registration/3D reconstruction and segmentation/classification of tissue regions from multi-channel data, (iii) and the visualization of the microstructure.
During this project week, we learn about ITK modules for registration, ITK filters and 3D Slicer. We will convert our algorithms into ITK modules and also into a pipeline in 3D slicer in the future.
