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Deep-learning based automatic localization and tracking of anatomical landmarks from cardiac magnetic resonange images
This project aims at developing a machine learning approach (for example, using convolutional neural networks) for localizing and tracking anatomical landmarks from cardiac MR images.
Keywords: machine learning, cardiac, magnetic resonance, image analysis, MRI, neural network, deep learning
Automatically and robustly extracting anatomical information from cardiac MR images is an area of ongoing research, which has potential application in a medical setting and in personalised modelling by using real anatomical morphology to construct a realistic synthetic anatomy, which can then be used in bio-physical simulations. We are developing a framework for fitting anatomical meshes to CMR data, and one key point for the correct quantification of clinical biomarkers is the identification of anatomical landmarks.
This project aims to make use of state of the art machine vision approaches to locate and track heart valves in cardiac MR images from 2D sets of images. The student will first evaluate existing solutions available in the literature and if necessary, will train a new neural network using our clinical dataset which will be manually processed to generate the requited training, validation and testing dataset.
In this project you will work extensively with python, numpy and pytorch, and prior experience with these is required. Additionally, prior experience with (cardiac) MRI or convolutional neural networks would be helpful.
Automatically and robustly extracting anatomical information from cardiac MR images is an area of ongoing research, which has potential application in a medical setting and in personalised modelling by using real anatomical morphology to construct a realistic synthetic anatomy, which can then be used in bio-physical simulations. We are developing a framework for fitting anatomical meshes to CMR data, and one key point for the correct quantification of clinical biomarkers is the identification of anatomical landmarks. This project aims to make use of state of the art machine vision approaches to locate and track heart valves in cardiac MR images from 2D sets of images. The student will first evaluate existing solutions available in the literature and if necessary, will train a new neural network using our clinical dataset which will be manually processed to generate the requited training, validation and testing dataset. In this project you will work extensively with python, numpy and pytorch, and prior experience with these is required. Additionally, prior experience with (cardiac) MRI or convolutional neural networks would be helpful.
The aim of this project is to develop a method that is able to automatically and robustly locate the heart valves in 2D cardiac MR images. Further, for time resolved data the method should be able to track the valves over the full cardiac cycle.
The aim of this project is to develop a method that is able to automatically and robustly locate the heart valves in 2D cardiac MR images. Further, for time resolved data the method should be able to track the valves over the full cardiac cycle.
Supervisors: Dr. Stefano Buoso (buoso@biomed.ee.ethz.ch). To apply for this project please email a copy of your CV and transcripts of your Bachelor and/or Master studies.
Supervisors: Dr. Stefano Buoso (buoso@biomed.ee.ethz.ch). To apply for this project please email a copy of your CV and transcripts of your Bachelor and/or Master studies.