High-throughput image cytometer for detection of circulating tumor cells and contrast-enhancement filtering for automated 3D image segmentation of cartilage tissue explants
by Nguyen, Lam K., Ph.D., UNIVERSITY OF CALIFORNIA, SAN DIEGO, 2007, 251 pages; 3259052

Abstract:

The first report of the presence of tumor cells circulating in blood was published over a century ago. The ability to routinely detect and characterize these circulating tumor cells can have a profound impact on diagnostic staging, therapy decisions, monitoring therapy efficacy, and long-term patient management. Existing methods of detection which are based on expression of proteins, are either plagued by high false positive rates or have not yet demonstrated consistent results. An alternative method of detection that exploits the morphologic differences between tumor and normal cells has great potential; however, there is not yet an instrument with sufficient speed and resolution to make this technique practical. A high-throughput, high-resolution image cytometer was developed to address this problem. A spiked cell model was used to measure its performance at finding circulating tumor cells and a performance comparison was made to an existing commercial image cytometer.

Better understanding of cartilage growth and repair is necessary for developing more effective therapy for articular cartilage repair. The confocal microscope permits experimental conditions that preserve the natural state of chondrocytes within the surrounding matrix. However, in the absence of any quantitative techniques for processing the data, the confocal microscope has been limited to a few qualitative roles. A contrast-enhancing technique was developed to enable automatic segmentation of confocal data sets for routine quantitative three-dimensional studies of thick cartilage tissue.

 
AdvisersJeffrey H. Price; Marcos Intaglietta
SchoolUNIVERSITY OF CALIFORNIA, SAN DIEGO
SourceDAI/B 68-04, p. , Jul 2007
Source TypeDissertation
SubjectsBiomedical engineering
Publication Number3259052
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