Difference between revisions of "2012 Summer Project Week:RidgeExtractionAtrialWallSegmentation"
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__NOTOC__ | __NOTOC__ | ||
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Image:PW-MIT2012.png|[[2012_Summer_Project_Week#Projects|Projects List]] | Image:PW-MIT2012.png|[[2012_Summer_Project_Week#Projects|Projects List]] | ||
Image:MRI1.jpg|Example of manual segmentation of the left atrial wall. | Image:MRI1.jpg|Example of manual segmentation of the left atrial wall. | ||
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==Background== | ==Background== | ||
| − | Catheter ablation has been proposed for treatment of atrial fibrillation arrhythmia. MRI data is used to explore lesion ablation and scarification locations are extracted from MRI image data. In addition, MRI analysis may help to predict if the ablation procedure will help a patient. | + | Catheter ablation has been proposed for treatment of atrial fibrillation arrhythmia. MRI data is used to explore lesion ablation and scarification locations are extracted from MRI image data. In addition, MRI analysis may help to predict if the ablation procedure will help a patient or not. |
Many of these image analysis tasks are largely based on segmentation of left atrial wall, which is done manually or semi-automatically. | Many of these image analysis tasks are largely based on segmentation of left atrial wall, which is done manually or semi-automatically. | ||
Automatic segmentation uses moving contours or surfaces (interfaces) to segment image data by minimizing a predefined energy function. | Automatic segmentation uses moving contours or surfaces (interfaces) to segment image data by minimizing a predefined energy function. | ||
| − | These moving interfaces are highly affected by image data, which can be thought as a force field pushing the interface to | + | These moving interfaces are highly affected by image data, which can be thought as a force field pushing the interface to features of choice. Thus, the choice of interface attracting image features is critical. |
==Key Investigators== | ==Key Investigators== | ||
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<h3>Objective</h3> | <h3>Objective</h3> | ||
| − | We are exploring different image features which are appropriate for automatic segmentation. Our objective is to compare different topological extrema ranking algorithms (including maximum persistence and perceptual ridge importance) to provide the most appropriate features, based on expert's manual segmentation. | + | We are exploring different image features which are appropriate for an automatic segmentation. Our objective is to compare different topological extrema ranking algorithms (including maximum persistence [1,2] and perceptual ridge importance) to provide the most appropriate features, based on expert's manual segmentation. |
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* Local ridge extraction | * Local ridge extraction | ||
* Topological persistence algorithm implementation | * Topological persistence algorithm implementation | ||
| − | * Comparison of different topological simplification approaches | + | * Comparison of different topological simplification approaches |
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==References== | ==References== | ||
| + | # Edelsbrunner, H., Harer, J., & Zomorodian, A. (2003). Hierarchical Morse--Smale Complexes for Piecewise Linear 2-Manifolds. Discrete and Computational Geometry, 30(1), 87-107. doi:10.1007/s00454-003-2926-5 | ||
| + | # Szymczak, A., Stillman, A., Tannenbaum, A., & Mischaikow, K. (2006). Coronary vessel trees from 3D imagery: a topological approach. Medical image analysis, 10(4), 548-59. doi:10.1016/j.media.2006.05.002 | ||
Revision as of 00:58, 8 June 2012
Home < 2012 Summer Project Week:RidgeExtractionAtrialWallSegmentation
Example of manual segmentation of the left atrial wall.
Background
Catheter ablation has been proposed for treatment of atrial fibrillation arrhythmia. MRI data is used to explore lesion ablation and scarification locations are extracted from MRI image data. In addition, MRI analysis may help to predict if the ablation procedure will help a patient or not. Many of these image analysis tasks are largely based on segmentation of left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses moving contours or surfaces (interfaces) to segment image data by minimizing a predefined energy function. These moving interfaces are highly affected by image data, which can be thought as a force field pushing the interface to features of choice. Thus, the choice of interface attracting image features is critical.
Key Investigators
- BU: Arie Nakhmani
- BU and CCC UAB: Allen Tannenbaum
Objective
We are exploring different image features which are appropriate for an automatic segmentation. Our objective is to compare different topological extrema ranking algorithms (including maximum persistence [1,2] and perceptual ridge importance) to provide the most appropriate features, based on expert's manual segmentation.
Approach, Plan
- Implementing the comparison framework
- Ellipsoidal MRI transform
- Local ridge extraction
- Topological persistence algorithm implementation
- Comparison of different topological simplification approaches
Progress
We have developed topological simplification methods for removing noisy local maxima. The resulting ridge detection algorithm shows average error less then 2 pixels compared to the manual segmentation.
Delivery Mechanism
This work will be delivered to the NA-MIC Kit as an Extension -- commandline
References
- Edelsbrunner, H., Harer, J., & Zomorodian, A. (2003). Hierarchical Morse--Smale Complexes for Piecewise Linear 2-Manifolds. Discrete and Computational Geometry, 30(1), 87-107. doi:10.1007/s00454-003-2926-5
- Szymczak, A., Stillman, A., Tannenbaum, A., & Mischaikow, K. (2006). Coronary vessel trees from 3D imagery: a topological approach. Medical image analysis, 10(4), 548-59. doi:10.1016/j.media.2006.05.002
