A New Model for the Segmentation of Multiple, Overlapping, Near-Circular Objects

Molnár, Csaba; Kató, Zoltán; Jermyn, Ian H.

doi:10.1109/dicta.2015.7371219

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The new model is constructed using the assumption that overlapping cells contribute additively to the image intensity, so that multiple cells on top of each other produce an intensity contribution that is a multiple of that of a single cell. (An early version of this work was presented recently 37 ).…”

Section: Methodsmentioning

confidence: 99%

Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours

Molnár

Jermyn

Kató

et al. 2016

Sci Rep

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The identification of fluorescently stained cell nuclei is the basis of cell detection, segmentation, and feature extraction in high content microscopy experiments. The nuclear morphology of single cells is also one of the essential indicators of phenotypic variation. However, the cells used in experiments can lose their contact inhibition, and can therefore pile up on top of each other, making the detection of single cells extremely challenging using current segmentation methods. The model we present here can detect cell nuclei and their morphology even in high-confluency cell cultures with many overlapping cell nuclei. We combine the “gas of near circles” active contour model, which favors circular shapes but allows slight variations around them, with a new data model. This captures a common property of many microscopic imaging techniques: the intensities from superposed nuclei are additive, so that two overlapping nuclei, for example, have a total intensity that is approximately double the intensity of a single nucleus. We demonstrate the power of our method on microscopic images of cells, comparing the results with those obtained from a widely used approach, and with manual image segmentations by experts.

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Section: Methodsmentioning

confidence: 99%

Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours

Molnár

Jermyn

Kató

et al. 2016

Sci Rep

Self Cite

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“…[3] introduces an end-toend approach with an encoder-decoder network, and [4] uses instance relation interaction. [5] presents a variational method for segmentation of near-circular shaped objects.…”

Section: Introductionmentioning

confidence: 99%

Multistar: Instance Segmentation Of Overlapping Objects With Star-Convex Polygons

Walter

Damrich

Hamprecht

2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

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Instance segmentation of overlapping objects in biomedical images remains a largely unsolved problem. We take up this challenge and present MultiStar, an extension to the popular instance segmentation method StarDist. The key novelty of our method is that we identify pixels at which objects overlap and use this information to improve proposal sampling and to avoid suppressing proposals of truly overlapping objects. This allows us to apply the ideas of StarDist to images with overlapping objects, while incurring only a small overhead compared to the established method. MultiStar shows promising results on two datasets and has the advantage of using a simple and easy to train network architecture.

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“…Let Yd be the (positive) weight of the data term; and let I be the intensity of the input image. Using the image formation model described earlier, and a Gaussian model for the image noise, the new data term becomes [74]: tJ.(I. 9+) = Yd i (I, 7 2 U AAf f + >2dx .…”

Section: Ata M Odel For Overlapping Ob Jects/cellsmentioning

confidence: 99%

Variational methods for shape modelling

Molnár

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A New Model for the Segmentation of Multiple, Overlapping, Near-Circular Objects

Cited by 5 publications

References 12 publications

Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours

Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours

Multistar: Instance Segmentation Of Overlapping Objects With Star-Convex Polygons

Variational methods for shape modelling

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