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Developing Deep Learning Algorithms for CT Bone Image Segmentation and Analysis
The aim of this project is to use and develop novel machine learning algorithms to segment high-resolution bone images and characterize cortical and trabecular bone structures. To this end, we aim to combine novel ideas tailored to cortical and trabecular bone structures with common segmentation practices and represent the trabecular bone structures as a graph representation with subsequent analysis.
Keywords: Segmentation, Deep Learning, Medical AI, Machine Learning, Computer Vision, Biomedical Image Analysis
The aim of this project is to use and develop novel machine learning algorithms to segment high-resolution bone images and characterize cortical and trabecular bone structures. To this end, we aim to combine novel ideas tailored to cortical and trabecular bone structures with common segmentation practices and represent the trabecular bone structures as a graph representation with subsequent analysis. For more details, please refer to the attached pdf document and its references.
It should be mentioned, that the internship/thesis will not be payed.
The aim of this project is to use and develop novel machine learning algorithms to segment high-resolution bone images and characterize cortical and trabecular bone structures. To this end, we aim to combine novel ideas tailored to cortical and trabecular bone structures with common segmentation practices and represent the trabecular bone structures as a graph representation with subsequent analysis. For more details, please refer to the attached pdf document and its references.
It should be mentioned, that the internship/thesis will not be payed.
Gain a better understanding of physiological and mechanical bone behavior via deep learning-based methods.
Gain a better understanding of physiological and mechanical bone behavior via deep learning-based methods.
Please send your CV and transcript to Bastian Wittmann (bastian.wittmann@uzh.ch), Serena Bonaretti (serena.bonaretti@balgristcampus.ch), and Bjoern Menze (bjoern.menze@uzh.ch).
Please send your CV and transcript to Bastian Wittmann (bastian.wittmann@uzh.ch), Serena Bonaretti (serena.bonaretti@balgristcampus.ch), and Bjoern Menze (bjoern.menze@uzh.ch).