Conclusions These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. Results To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed. Many of these algorithms have been designed for specific tasks and validated with limited image data. Finally our sephaCe application, which is available at, provides a novel method for integrating these methods with any motorised microscope, thus facilitating the adoption of these techniques in biological research labs.īackground Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Together, this suite of algorithms permit brightfield microscopy image processing without the need for additional fluorescence images. The algorithms for cell detection and nucleus segmentation are novel to the field, whilst the cell boundary segmentation algorithm is contrast-invariant, which makes it more robust on these low-contrast images. When tested on HT1080 and HeLa cells, the cell detection step was able to correctly identify over 80% of cells, whilst the cell boundary segmentation step was able to segment over 75% of the cell body pixels, and the nucleus segmentation step was able to correctly identify nuclei in over 75% of the cells.
#CELLPROFILER ZERNIKE MANUAL#
The algorithms were evaluated on a variety of biological cell lines and compared against manual and fluorescence-based ground truths.
#CELLPROFILER ZERNIKE REGISTRATION#
The package also performs image registration between brightfield and fluorescence images.
#CELLPROFILER ZERNIKE FREE#
This paper presents a free and open-source image analysis package which fully automates the tasks of cell detection, cell boundary segmentation, and nucleus segmentation in brightfield images. The detection and segmentation of adherent eukaryotic cells from brightfield microscopy images represent challenging tasks in the image analysis field.
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