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Segmentation of Aortic Centrelines from MRI survey data
The aim of this project is to segment aortic centrelines from low-resolution MRI data to improve automatic slice planning.
Keywords: MRI, Segmentation, Neural Networks, U-net, Python, Digital Twinning, Machine Learning, Data Analysis, Aortic Stenosis Aorta
This project provides a solution for automated segmentation of the aortic centerline from MRI survey data, providing valuable information for subsequent positioning of slices during cardiac examination.
This project provides a solution for automated segmentation of the aortic centerline from MRI survey data, providing valuable information for subsequent positioning of slices during cardiac examination.
The aim of this projec is to develop a robust method, using either traditional segmentation methods with open-source Python libraries, such as scikit-learn, or advanced neural network approaches to automatically segment aortic centrelines from low-resolution MRI survey data. The student is asked to compare several methods for segmentation, which, in case of a deep learning approach, inlucludes generation of segmentation mask in ITK-SNAP.
The project will be carried out in Python and make use of open-source image analysis libraries such as scikit-learn and Pyvista.
The aim of this projec is to develop a robust method, using either traditional segmentation methods with open-source Python libraries, such as scikit-learn, or advanced neural network approaches to automatically segment aortic centrelines from low-resolution MRI survey data. The student is asked to compare several methods for segmentation, which, in case of a deep learning approach, inlucludes generation of segmentation mask in ITK-SNAP. The project will be carried out in Python and make use of open-source image analysis libraries such as scikit-learn and Pyvista.
Please contact Gloria Wolkerstorfer (wolkerstorfer@biomed.ee.ethz.ch) for further information. To apply for this project, please email a copy of your CV and transcripts of your Bachelor and/or Master studies.
Please contact Gloria Wolkerstorfer (wolkerstorfer@biomed.ee.ethz.ch) for further information. To apply for this project, please email a copy of your CV and transcripts of your Bachelor and/or Master studies.
