Cancer cell segmentation and data extraction
Sandberg, Ossi (2018)
2018
Automaatiotekniikka
Teknisten tieteiden tiedekunta – Faculty of Engineering Sciences
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Hyväksymispäivämäärä
2018-11-07
Julkaisun pysyvä osoite on
http://urn.fi/URN:NBN:fi:tty-201810242435
http://urn.fi/URN:NBN:fi:tty-201810242435
Tiivistelmä
Cancer is a health problem affecting approximately 14 million people each year. One of the causes of cancer when normal cell life is disturbed and the cell does not die as programmed. In addition to this, as cell research requires immortalized cells, cancer cell provides a good ground to research mammalian cells.
Fluorescence is an important tool in biological research. Fluorescence images can be captured with specialized microscope. These images require more processing to extract countable data. Instead of microscope imagas, flow cytometry can be used to calculate fluorescence in each cell. Flow cytometry loses information in comparison to microscopy, so it cannot replace microscopy completely. However, microscopy image can contain all the same information that flow cytometry techniques can.
This thesis is a literature review on how fluorescence data is extracted from a cancer cell. The review was conducted in two parts. In first part, recently developed methods were examined and in second part, methods currently are in use were reviewed. Segmentation and microscopy methods seemed prevalent on method research but fluorescence-activated cell sorting was most used.
Fluorescence-activated cell sorting is a common flow cytometry technique used even though it loses information of location and morphology. This seems to be due to unambiguous data it produces. Microscopy methods are less in use but are still used and segmentation is performed but typically with custom algorithms. Segmentation is a difficult task that requires specific expertise, which might be the reason that it is not in wider use in recent publications. Work is needed to close cap between biologist and image analyst.
Fluorescence is an important tool in biological research. Fluorescence images can be captured with specialized microscope. These images require more processing to extract countable data. Instead of microscope imagas, flow cytometry can be used to calculate fluorescence in each cell. Flow cytometry loses information in comparison to microscopy, so it cannot replace microscopy completely. However, microscopy image can contain all the same information that flow cytometry techniques can.
This thesis is a literature review on how fluorescence data is extracted from a cancer cell. The review was conducted in two parts. In first part, recently developed methods were examined and in second part, methods currently are in use were reviewed. Segmentation and microscopy methods seemed prevalent on method research but fluorescence-activated cell sorting was most used.
Fluorescence-activated cell sorting is a common flow cytometry technique used even though it loses information of location and morphology. This seems to be due to unambiguous data it produces. Microscopy methods are less in use but are still used and segmentation is performed but typically with custom algorithms. Segmentation is a difficult task that requires specific expertise, which might be the reason that it is not in wider use in recent publications. Work is needed to close cap between biologist and image analyst.