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Neural Implicit Fields for Representing Cardiovascular Organs
This project revolves around creating implicit neural representations of cardiovascular organs for application to virtual patient generation.
Keywords: Implicit neural representations, Multi-layer perceptrons, Signed distance fields, Shape representation, deep learning.
In the realm of biomedical engineering and computational anatomy, the generation of synthetic 3D cardiovascular anatomy holds immense potential for advancing in-silico trials of device deployment. However, the complexity and volumetric nature of such data present substantial challenges for conventional 3D models. This master's thesis project proposes to overcome these limitations by developing an implicit neural representation (INR) of 3D cardiovascular organs. INRs have demonstrated superior capabilities in capturing intricate shape details and offer greater scalability compared to traditional approaches. The core of this project involves not only the development and optimization of the INR framework but also its integration with existing generative models of cardiovascular anatomy. The student will benchmark the developed system against current state-of-the-art models to demonstrate its efficacy and advantages in the field.
**Prerequisites**
• Substantial experience in deep learning methodologies.
• Proficiency in computational geometry and mesh processing.
• A strong background in image processing.
**Good to have**
• Expertise in the field of computer vision.
• Experience working with implicit neural representations.
• Knowledge and experience in shape analysis.
In the realm of biomedical engineering and computational anatomy, the generation of synthetic 3D cardiovascular anatomy holds immense potential for advancing in-silico trials of device deployment. However, the complexity and volumetric nature of such data present substantial challenges for conventional 3D models. This master's thesis project proposes to overcome these limitations by developing an implicit neural representation (INR) of 3D cardiovascular organs. INRs have demonstrated superior capabilities in capturing intricate shape details and offer greater scalability compared to traditional approaches. The core of this project involves not only the development and optimization of the INR framework but also its integration with existing generative models of cardiovascular anatomy. The student will benchmark the developed system against current state-of-the-art models to demonstrate its efficacy and advantages in the field.
**Prerequisites**
• Substantial experience in deep learning methodologies. • Proficiency in computational geometry and mesh processing.
• A strong background in image processing.
**Good to have**
• Expertise in the field of computer vision.
• Experience working with implicit neural representations.
• Knowledge and experience in shape analysis.
• Cultivating and managing diverse cardiovascular shape datasets.
• Developing and fine-tuning the INR framework to accurately represent 3D cardiovascular structures.
• Integrating the INR framework with generative models specifically designed for cardiovascular anatomy.
• Cultivating and managing diverse cardiovascular shape datasets.
• Developing and fine-tuning the INR framework to accurately represent 3D cardiovascular structures.
• Integrating the INR framework with generative models specifically designed for cardiovascular anatomy.
Interested candidates should send CV and cover letter to kkadry@mit.edu
Interested candidates should send CV and cover letter to kkadry@mit.edu
