In biology, many different kinds of microscopy are used to study cells. There are many different kinds of transmission microscopy, where light is passed through the cells, that can be used without staining or other treatments that can harm the cells. There is also fluorescence microscopy, where fluorescent proteins or dyes are placed in the cells or in parts of the cells, so that they emit light of a specific wavelength when they are illuminated with light of a different wavelength. Many fluorescence microscopes can take images on many different depths in a sample and thereby build a three-dimensional image of the sample. Fluorescence microscopy can also be used to study particles, for example viruses, inside cells. Modern microscopes often have digital cameras or other equipment to take images or record time-lapse video.
When biologists perform experiments on cells, they often record image sequences or sequences of three-dimensional volumes to see how the cells behave when they are subjected to different drugs, culture substrates, or other external factors. Previously, the analysis of recorded data has often been done manually, but that is very time-consuming and the results often become subjective and hard to reproduce. Therefore there is a great need for technology for automated analysis of image sequences with cells and particles inside cells. Such technology is needed especially in biological research and drug development. But the technology could also be used clinically, for example to tailor a cancer treatment to an individual patient by evaluating different treatments on cells from a biopsy.
This thesis presents algorithms to find cells and particles in images, and to calculate tracks that show how they have moved during an experiment. We have developed a complete system that can find and track cells in all commonly used imaging modalities. We selected and extended a number of existing segmentation algorithms, and thereby created a complete tool to find cell outlines. To link the segmented objects into tracks, we developed a new track linking algorithm. The algorithm adds tracks one by one using dynamic programming, and has many advantages over prior algorithms. Among other things, it is fast, it calculates tracks which are optimal for the entire image sequence, and it can handle situations where multiple cells have been segmented incorrectly as one object. To make it possible to use information about the velocities of the objects in the linking, we developed a method where the positions of the objects are preprocessed using a filter before the linking is performed. This is important for tracking of some particles inside cells and for tracking of cell nuclei in some embryos.
We have developed an open source software which contains all tools that are necessary to analyze image sequences with cells or particles. It has tools for segmentation and tracking of objects, optimization of settings, manual correction, and analysis of outlines and tracks. We developed the software together with biologists who used it in their research. The software has already been used for data analysis in a number of biology publications. Our system has also achieved outstanding performance in three international objective comparisons of systems for tracking of cells.