Dense nuclei segmentation based on graph cut and convexity–concavity analysis

Qi, Jin

doi:10.1111/jmi.12096

Cited by 23 publications

(11 citation statements)

References 21 publications

(87 reference statements)

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“…A large pool of powerful cell nuclei segmentation methods exists, including iterative thresholding24, level sets25, graph cut26, gradient flow tracking27, lines-of-sight28 or watershed methods29. Identifying the approach that is appropriate for a wide variety of datasets, fast and robust with respect to high cell density, as well as variations in cell nuclei volume, shape and dye distribution, has become a major challenge in image analysis.…”

mentioning

confidence: 99%

Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids

Schmitz¹,

Fischer²,

Mattheyer³

et al. 2017

Sci Rep

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Three-dimensional multicellular aggregates such as spheroids provide reliable in vitro substitutes for tissues. Quantitative characterization of spheroids at the cellular level is fundamental. We present the first pipeline that provides three-dimensional, high-quality images of intact spheroids at cellular resolution and a comprehensive image analysis that completes traditional image segmentation by algorithms from other fields. The pipeline combines light sheet-based fluorescence microscopy of optically cleared spheroids with automated nuclei segmentation (F score: 0.88) and concepts from graph analysis and computational topology. Incorporating cell graphs and alpha shapes provided more than 30 features of individual nuclei, the cellular neighborhood and the spheroid morphology. The application of our pipeline to a set of breast carcinoma spheroids revealed two concentric layers of different cell density for more than 30,000 cells. The thickness of the outer cell layer depends on a spheroid’s size and varies between 50% and 75% of its radius. In differently-sized spheroids, we detected patches of different cell densities ranging from 5 × 105 to 1 × 106 cells/mm3. Since cell density affects cell behavior in tissues, structural heterogeneities need to be incorporated into existing models. Our image analysis pipeline provides a multiscale approach to obtain the relevant data for a system-level understanding of tissue architecture.

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mentioning

confidence: 99%

Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids

Schmitz¹,

Fischer²,

Mattheyer³

et al. 2017

Sci Rep

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show abstract

“…One reason for the robustness is, that the decomposition is performed directly on the three-dimensional objects rather than applying it on each two-dimensional slice independently like in [ 47 ] or [ 19 ]. Incorporating the three-dimensional information provides more accurate decomposition of apparently touching cell nuclei.…”

Section: Pre-processingmentioning

confidence: 99%

“…Three-dimensional cell nuclei segmentation methods commonly rely on pre-processing steps (e.g. filtering, initial thresholding or seed detection) combined with watershed ([ 15 – 17 ]), graph cut ([ 18 , 19 ]), machine learning ([ 20 , 21 ]), gradient flow tracking [ 22 ], active surface models [ 23 ], level set [ 24 ], or concavity-based segmentation ([ 25 , 26 ]). But despite the continual progress in three-dimensional cell nuclei segmentation, there is still a need to improve accuracy, level of automation and adaptability.…”

Section: Introductionmentioning

confidence: 99%

Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition

et al. 2015

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BackgroundDue to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset.ResultsWe present an advanced three-dimensional cell nuclei segmentation algorithm that is accurate and robust. Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. We demonstrate the superior performance of our algorithm using data from different specimens recorded with different microscopes. The three-dimensional images were recorded with confocal and light sheet-based fluorescence microscopes. The specimens are an early mouse embryo and two different cellular spheroids. We compared the segmentation accuracy of our algorithm with ground truth data for the test images and results from state-of-the-art methods. The analysis shows that our method is accurate throughout all test datasets (mean F-measure: 91 %) whereas the other methods each failed for at least one dataset (F-measure ≤ 69 %). Furthermore, nuclei volume measurements are improved for LoS decomposition. The state-of-the-art methods required laborious adjustments of parameter values to achieve these results. Our LoS algorithm did not require parameter value adjustments. The accurate performance was achieved with one fixed set of parameter values.ConclusionWe developed a novel and fully automated three-dimensional cell nuclei segmentation method incorporating LoS decomposition. LoS are easily accessible features that ensure correct splitting of apparently touching cell nuclei independent of their shape, size or intensity. Our method showed superior performance compared to state-of-the-art methods, performing accurately for a variety of test images. Hence, our LoS approach can be readily applied to quantitative evaluation in drug testing, developmental and cell biology.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0617-x) contains supplementary material, which is available to authorized users.

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“…Mathematical morphology (Nedzved et al, 2000;Shu et al, 2013) can be applied to segment partially touching cells using the opening operation or the ultimate residues, but only strives in low-density regions. Approach based on concavity detection (Bai et al, 2008;Kothari et al, 2009;Zhang et al, 2013;Qi, 2014;Riccio et al, 2019) allows concave points on the contours of touching cells to be detected. Touching cells can be optimally separated using ellipse registration or a distance transformation algorithm, but false concave points due to noise are often present.…”

Section: Introductionmentioning

confidence: 99%

Automated Individualization of Size-Varying and Touching Neurons in Macaque Cerebral Microscopic Images

et al. 2019

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In biomedical research, cell analysis is important to assess physiological and pathophysiological information. Virtual microscopy offers the unique possibility to study the compositions of tissues at a cellular scale. However, images acquired at such high spatial resolution are massive, contain complex information, and are therefore difficult to analyze automatically. In this article, we address the problem of individualization of size-varying and touching neurons in optical microscopy two-dimensional (2-D) images. Our approach is based on a series of processing steps that incorporate increasingly more information. (1) After a step of segmentation of neuron class using a Random Forest classifier, a novel min-max filter is used to enhance neurons' centroids and boundaries, enabling the use of region growing process based on a contour-based model to drive it to neuron boundary and achieve individualization of touching neurons. (2) Taking into account size-varying neurons, an adaptive multiscale procedure aiming at individualizing touching neurons is proposed. This protocol was evaluated in 17 major anatomical regions from three NeuN-stained macaque brain sections presenting diverse and comprehensive neuron densities. Qualitative and quantitative analyses demonstrate that the proposed method provides satisfactory results in most regions (e.g., caudate, cortex, subiculum, and putamen) and outperforms a baseline Watershed algorithm. Neuron counts obtained with our method show high correlation with an adapted stereology technique performed by two experts (respectively, 0.983 and 0.975 for the two experts). Neuron diameters obtained with our method ranged between 2 and 28.6 µm, matching values reported in the literature. Further works will aim to evaluate the impact of staining and interindividual variability on our protocol.

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Dense nuclei segmentation based on graph cut and convexity–concavity analysis

Cited by 23 publications

References 21 publications

Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids

Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids

Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition

Automated Individualization of Size-Varying and Touching Neurons in Macaque Cerebral Microscopic Images

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