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Interactive Multi-Tissue Segmentation Platform for Intravascular Imaging
This masters thesis project revolves around developing a rapid annotation platform for the creation of patient-specific digital twins from intravascular imaging.
Keywords: Deep learning, Computer Vision, Segmentation, Foundation models, Digital twins, Active learning.
The development of digital twins of coronary arteries is a pivotal advancement in understanding and managing coronary artery disease. These digital representations, created from intravascular imaging segmentations such as optical coherence tomography (OCT), enable the simulation of various biophysical dynamics pertinent to the pathophysiology and intervention strategies of coronary artery disease. However, a critical challenge lies in the generalization of automated segmentation algorithms across different OCT systems and diverse coronary pathologies. This master's thesis project aims to address this gap by developing an interactive multi-tissue segmentation platform specifically tailored for intravascular images. The platform will facilitate the rapid creation of high-quality segmentations for various arterial tissues, crucial for the creation of accurate 3D digital twins for biophysical simulations. Moreover, the project emphasizes the efficiency of the segmentation process, ensuring both precision in segmentation and expeditious quality control.
**Prerequisites**
• Proficiency in deep learning frameworks.
• Strong software engineering skills.
• A comprehensive understanding of image processing techniques.
**Good to have**
• Familiarity with medical imaging modalities.
• Experience with 3D segmentation software.
The development of digital twins of coronary arteries is a pivotal advancement in understanding and managing coronary artery disease. These digital representations, created from intravascular imaging segmentations such as optical coherence tomography (OCT), enable the simulation of various biophysical dynamics pertinent to the pathophysiology and intervention strategies of coronary artery disease. However, a critical challenge lies in the generalization of automated segmentation algorithms across different OCT systems and diverse coronary pathologies. This master's thesis project aims to address this gap by developing an interactive multi-tissue segmentation platform specifically tailored for intravascular images. The platform will facilitate the rapid creation of high-quality segmentations for various arterial tissues, crucial for the creation of accurate 3D digital twins for biophysical simulations. Moreover, the project emphasizes the efficiency of the segmentation process, ensuring both precision in segmentation and expeditious quality control.
**Prerequisites**
• Proficiency in deep learning frameworks.
• Strong software engineering skills.
• A comprehensive understanding of image processing techniques.
**Good to have**
• Familiarity with medical imaging modalities.
• Experience with 3D segmentation software.
• Organizing and managing intravascular imaging datasets.
• Developing and refining interactive segmentation models, along with adapting foundational models for specific requirements.
• Benchmarking the segmentation process, focusing on both the time efficiency and quality of the segmentations obtained.
• Organizing and managing intravascular imaging datasets.
• Developing and refining interactive segmentation models, along with adapting foundational models for specific requirements.
• Benchmarking the segmentation process, focusing on both the time efficiency and quality of the segmentations obtained.
Interested candidates should send CV and cover letter to kkadry@mit.edu
Interested candidates should send CV and cover letter to kkadry@mit.edu
