Research Supervisor Connect

Intelligent Multimodality Molecular Image Segmentation


Automated segmentation of major structures, tissues, and volume of interest (VOI) from PET/CT images new knowledge from information technologies.


Professor David Feng.

Research location

Computer Science

Program type



he ever-increasing amounts of molecular images produced in hospitals around the world require intensive efforts of proficient physicians to manually delineate structures and region-of-interest (ROI) from them. This manual processing is laborious and operator dependent and thus prone to reproducibility errors. Automated image segmentation is traditionally a difficult task in image processing. However, the newly emerged multimodality molecular images have brought great potentials to the research of image segmentation, since they contain complementary information from diverse imaging modalities. The aim of this project is to use PET-CT images as our case study to investigate novel segmentation approaches for multimodality molecular images.

The output of this project will be a segmentation algorithm toolbox, which will facilitate molecular image visualization, retrieval, and computer aided diagnosis (CAD). Students involved in the projects will have the opportunity to work in RPA hospital for a certain period of time depending on the progress and necessarily. By finishing this project, students will learn some state of the art technologies in both medical imaging and multimedia.

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Opportunity ID

The opportunity ID for this research opportunity is 385

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