Cancer is one of the top killers in Hong Kong and the other world. Conventional cancer diagnostics using invasive biopsy and/or positron emission tomography in hospitals is expensive and time-consuming. Circulation tumor cells (CTCs) in human blood has been shown to be one new liquid biopsy for cancer diagnostics. The Microfluidic Elasto-Filtration (MEF) Chips have been used for personalized detection of CTCs in the clinical study in the collaborative hospital, Sun-Yat Sun University Cancer Center in Guangzhou, China. However, the fluorescence microscopy of CTCs captured by MEF chips is labor-intensive and time-consuming. This project is to apply Artificial Intelligence, Deep Convolutional Neural Network (DCNN) algorithm, to automatically learned features for fluorescence microscopy of captured CTC.
The student(s) will develop a computer program to analyze the fluorescence micrographs obtained from the multi-color fluorescence microscopy of microfluidic elasto-filtration (MEF) CTC chips for cancer diagnosis. The MEF CTC chips will be fabricated at HKUST Nanosystem Fabrication Facility. The program will perform the following tasks: counting of white blood cells (WBC), generate the histogram of WBC size; counting of CTC and plot the histogram of CTC size and then apply the open-source Deep Convolutional Neural Network (DCNN) to process the fluorescence micrographs of CTCs to achieve the new automatic detection of CTC based on FDA-approved immunofluorescence assay and the nuclear-cytoplasmic ratio(NC ratio).
Develop an understanding of cancer and CTCs
Develop an understanding of multi-color fluorescence microscopy for CTCs
Develop computational skills to design and analyze the digital fluorescence micrographs from MEF chips.
Develop the skill to apply open-source Deep Convolutional Neural Network (DCNN) program to process the fluorescence micrographs of CTCs
Develop and practice technical communication skill