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

TY - JOUR

T1 - Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images

AU - Ali, Rehan

AU - Gooding, Mark

AU - Szilagyi, Tuende

AU - Vojnovic, Boris

AU - Christlieb, Martin

AU - Brady, Michael

PY - 2012/7

Y1 - 2012/7

N2 - 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.

AB - 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.

KW - Segmentation

KW - Registration

KW - Cell detection

KW - Level sets

KW - Monogenic signal

KW - Continuous intrinsic dimensionality

KW - PHASE-BASED SEGMENTATION

KW - LIVING CELLS

KW - RETRIEVAL

KW - TOOL

U2 - 10.1007/s00138-011-0337-9

DO - 10.1007/s00138-011-0337-9

M3 - Article

SN - 0932-8092

VL - 23

SP - 607

EP - 621

JO - MACHINE VISION AND APPLICATIONS

JF - MACHINE VISION AND APPLICATIONS

IS - 4

M1 - N/A

ER -