Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images

作者：Ali Rehan^*; Gooding Mark; Szilagyi Tuende; Vojnovic Borivoj; Christlieb Martin; Brady Michael

来源：Machine Vision and Applications, 2012, 23(4): 607-621.

DOI：10.1007/s00138-011-0337-9

• Segmentation
• Registration
• Cell detection
• Level sets
• Monogenic signal
• Continuous intrinsic dimensionality

The detection and segmentation of adherent eukaryotic cells from brightfield microscopy images represent challenging tasks in the image analysis field. 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 package also performs image registration between brightfield and fluorescence images. The algorithms were evaluated on a variety of biological cell lines and compared against manual and fluorescence-based ground truths. 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. 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. Together, this suite of algorithms permit brightfield microscopy image processing without the need for additional fluorescence images. Finally our sephaCe application, which is available at http://www.sephace.com, provides a novel method for integrating these methods with any motorised microscope, thus facilitating the adoption of these techniques in biological research labs.

• 出版日期2012-7