RCTL research PhD projects.

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Presentation transcript:

RCTL research PhD projects

2nd PhD: Sten Roar Snare Graduated from NTNU in 2011. Contributions: Added 2D segm. support Added spline contour support. Applications: 2D multi-chamber segm. 2D septum thickness segm. US view detection & scoring

Snare & al.: Automated Septum Thickness Measurement - A Kalman Filter Approach, Comp. Met. & Prog. in Biomedicine 2011. Setup: 2D ultrasound data. Three connected spline curves that track both sides of the septum wall and the mitral valve. Application: Automatic measurement of the septum wall thickness (indicator of too high blood pressure). Automatic algorithm vs. reference.

Snare & al.: Real-time Scan Assistant for Echocardiography, IEEE UFFC, 2012 Setup: Automatic fitting of 4 connected models, corresponding to the 4 cardiac chambers. Scoring of the segmentation “fitness” to asses model vs. data agreement. Application: Make ultrasound easier to use. Proved real-time feedback on the extent of which the desired anatomy (4 cardiac chambers) is really imaged.

3rd PhD: Engin Dikici Graduated from NTNU in 2013. Contributions: Improved edge-detection (graph-cut etc.) Added FEM-based regularization of multi-scale segm. Added bidirectional segm. Applications: 3D US LV segm.

Dikici & al.: Graph-Cut Based Edge Detection for Kalman Filter Based Left Ventricle Tracking in 3D+T, Echo, IEEE Computing in Cardiology 2010 Setup: Create a graph by connecting the edge-detection profiles. Use min flow/max cut algorithm to simultaneously detect the «optimal» edge in all edge profiles. Application: Improved edge detection in 3D US LV segmentation.

Dikici & al: Generalized Step Criterion Edge Detectors for Kalman Filter Based Left Ventricle Tracking in 3D+T Echoc, Stat. Atlases and Comp. Models of the Heart 2012 Setup: Piecewise constant, linear and quadratic regression of surface-normal intensity profiles. Weighted sum of detected edges with const/linear/quadratic regression,based on trained weighting parameters. Application: Improved edge-detection in 3D ultrasound segmentation. Intensity profile (red), regression-lines (black), cost function (green), detected edge (blue)

Dikici & al.: Doo-Sabin Surface Models with Biomechanical Constraints for Kalman Filter Based Endocardial Wall Tracking in 3D+T Echocardiography, British Machine Vision Conference 2012 Setup: Finite element method-based regularization of meshes (stiffness matrix). Application: Improved and consistent regularization model behavior across scales. FEM simulations of biomechanical regularization, independent of scale/resolution.

Dikici & al.: Doo-Sabin Surface Models with Biomechanical Constraints for Kalman Filter Based Endocardial Wall Tracking in 3D+T Echocardiography, British Machine Vision Conference (BMVC), 2012 Setup: Use stiffness matrices from FEM modeling to regularize model during segmentation. Application: Improved and consistent regularization model behavior across scales.

PhD projects in-progress 4th PhD: Sigurd Storve (NTNU) Combine image with tissue-velocity data to update model. Application: Improved 2D US LV segmentation. 5th PhD: Jørn Bersvendsen (GEVU/CCI) 3D US aortic outlet segmentation. Application: Aortic diameter for surgery planning. 6th PhD: Pavan Annangi (GE Global research, Bangalore) Multi-slice (2.5D) US bladder segmentation. Several others are also using the software as part of their research.