A Unified Framework for Automated 3-D Segmentation of Surface-Stained Living Cells and a Comprehensive Segmentation Evaluation

Hodneland, Erlend; Bukoreshtliev, Nickolay V.; Eichler, Tilo Wolf; Gurke, Steffen; Lundervold, Arvid; Gerdes, Hans‐Hermann

doi:10.1109/tmi.2008.2011522

Cited by 33 publications

(28 citation statements)

References 58 publications

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“…Figure 3). The markers are found automatically in by adaptive thresholding, with the various steps explained in more detail in [23]. The available options are finding markers (i) automatically from the segmentation image (, default ), (ii) from the nucleus image using (), or (iii) manually ( with the option and /or specified as argument to , supplying the manually defined markers).…”

Section: Results - Basic Principles and Cellsegm Functionsmentioning

confidence: 99%

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

Hodneland

Kögel

Frei

et al. 2013

Source Code Biol Med

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The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening.

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Section: Results - Basic Principles and Cellsegm Functionsmentioning

confidence: 99%

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

Hodneland

Kögel

Frei

et al. 2013

Source Code Biol Med

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“…In order to evaluate the goodnesss of the automatic segmentation we conducted a full kidney segmentation (ii) of both kidneys in all subjects by manual expert delineation (E.E.). The overlap between the manual segmentation and the automated segmentations was measured as described in [23]. For this approach the goodness of segmentation p for a manually segmented ROI R m and an automatically segmented ROI R a is expressed as…”

Section: Discussionmentioning

confidence: 99%

Segmentation-Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys

Hodneland

Hanson

Lundervold

et al. 2014

IEEE Trans. on Image Process.

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Abstract-Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the kidneys requires proper motion correction and segmentation to enable an estimation of glomerular filtration rate through pharmacokinetic modeling. Traditionally, co-registration, segmentation, and pharmacokinetic modeling have been applied sequentially as separate processing steps. In this paper, a combined 4D model for simultaneous registration and segmentation of the whole kidney is presented. To demonstrate the model in numerical experiments, we used normalized gradients as data term in the registration and a Mahalanobis distance from the time courses of the segmented regions to a training set for supervised segmentation. By applying this framework to an input consisting of 4D image time series, we conduct simultaneous motion correction and two-region segmentation into kidney and background. The potential of the new approach is demonstrated on real DCE-MRI data from ten healthy volunteers.

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“…Similar to UE, this conditional erosion is defined on binary images, and meanwhile it requires two extra predefined thresholds for termination, which need to be carefully selected for different pathology and microscopy images. Hodneland et al [36] have proposed a morphology operation based cell detection approach on 3D fluorescence images. It first applies adaptive threshold [56] to ridge extraction, and then performs iterative close operation with an increasing-radius circular structure element to link the gaps in the binary edges.…”

Section: Nucleus and Cell Detection Methodsmentioning

confidence: 99%

“…Yang et al [206] have applied marker-controlled watershed to gradient magnitude images for cell-like particle segmentation in low-SNR fluorescence images, in which markers can be easily detected on the noise-reduced gray-scale images with feature-preserving nonlocal means filtering. With supervised learning based markers detection in [123], watershed transform is applied to overlapping nuclei separation in histology images; in [36], marker-controlled watershed transform is applied to ridge-enhanced intensity images for cell segmentation in 3D fluorescence microscopy images. Recently, watershed transform is applied to cell segmentation in a wavelet coefficient space of fluorescence microscopy images, in which noise has been significantly reduced [184].…”

Section: Nucleus and Cell Segmentation Methodsmentioning

confidence: 99%

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Xing

Yang

2016

IEEE Rev. Biomed. Eng.

443

220

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Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to inter-observer variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literatures. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast (DIC), fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

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A Unified Framework for Automated 3-D Segmentation of Surface-Stained Living Cells and a Comprehensive Segmentation Evaluation

Cited by 33 publications

References 58 publications

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

Segmentation-Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

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