Automated evaluation of her-2/neu status in breast tissue from fluorescent in situ hybridization images

Raimondo, Francesco Di; Gavrielides, Marios A.; Karayannopoulou, Georgia; Lyroudia, Kleoniki; Pitas, Ioannis; Kostopoulos, Ioannis

doi:10.1109/tip.2005.852806

Cited by 60 publications

(52 citation statements)

References 18 publications

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“…To detect FISH spots, a 320 objective might be appropriate (28,29); however, if not only the number and intensity of signals are important but their spatial pattern as well, the application of a higher magnification and more importantly high resolution is desirable (30)(31)(32). Considering the prior, several workgroups use 360, 363, or 3100 objectives during the automated i-FISH analysis (21,(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43). An alternative approach, also increasing speed, is the use of two objectives, one of them for nucleus selection (objective magnification: and the other one for detecting FISH signals (objective magnification: 340-100) (31,44-52).…”

Section: Machine-assisted Evaluationmentioning

confidence: 99%

“…After primary segmentation, cell debris and objects with non-specific counterstain have to be excluded from the further analysis while there are controversial considerations regarding cell aggregates. Some groups prefer to separate nuclei composing aggregates using, for example, a watershed algorithm (23,34,35,37,56) while others exclude the clusters entirely (29,30,41,(57)(58)(59). The prior decreases the rate of cell loss; however, separation of touching nuclei is also a source of error because the process often cuts single cells with segmented nuclei (e.g., granulocytes) into pieces.…”

Section: Sequence Of Analysismentioning

confidence: 99%

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State‐of‐the‐art FISHing: Automated analysis of cytogenetic aberrations in interphase nuclei

et al. 2012

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Interphase fluorescence in situ hybridization (i‐FISH) is a powerful tool for visualizing various molecular targets in non‐dividing cells. Manual scoring of i‐FISH signals is a labor intensive, time‐consuming, and error‐prone process liable to subjective interpretation. Automated evaluation of signal patterns provides the opportunity to overcome these difficulties. The first report on automated i‐FISH analysis has been published 20 years ago and since then several applications have been introduced in the fields of oncology, and prenatal and fertility screening. In this article, we provide an insight into the automated i‐FISH analysis including its course, brief history, clinical applications, and advantages and challenges. The lack of guidelines for describing new automated i‐FISH methods hampers the precise comparison of performance of various applications published, thus, we make a proposal for a panel of parameters essential to introduce and standardize new applications and reproduce previously described technologies. © 2012 International Society for Advancement of Cytometry

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Section: Machine-assisted Evaluationmentioning

confidence: 99%

Section: Sequence Of Analysismentioning

confidence: 99%

State‐of‐the‐art FISHing: Automated analysis of cytogenetic aberrations in interphase nuclei

et al. 2012

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“…They select this h value experimentally (14,17) or by optimizing a criterion function (16). Once it is selected, this value is used for the entire image or the corresponding connected component.…”

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confidence: 99%

Iterative h‐minima‐based marker‐controlled watershed for cell nucleus segmentation

et al. 2016

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Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The markercontrolled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. V C 2016 International Society for Advancement of Cytometry

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“…In particular, methods based on mathematical morphology are very popular, which detect the spots based on their shape [e.g., using the top-hat transform (22,23)] and/or their intensity [e.g., HDome transform (24), EMax transform (25)]. Adaptive thresholding methods analyzing the spot height and its size were proposed in (26)(27)(28).…”

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confidence: 99%

Performance and sensitivity evaluation of 3D spot detection methods in confocal microscopy

Štěpka

Matula

et al. 2015

Cytometry Pt A

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Reliable 3D detection of diffraction-limited spots in fluorescence microscopy images is an important task in subcellular observation. Generally, fluorescence microscopy images are heavily degraded by noise and non-specifically stained background, making reliable detection a challenging task. In this work, we have studied the performance and parameter sensitivity of eight recent methods for 3D spot detection. The study is based on both 3D synthetic image data and 3D real confocal microscopy images. The synthetic images were generated using a simulator modeling the complete imaging setup, including the optical path as well as the image acquisition process. We studied the detection performance and parameter sensitivity under different noise levels and under the influence of uneven background signal. To evaluate the parameter sensitivity, we propose a novel measure based on the gradient magnitude of the F 1 score. We measured the success rate of the individual methods for different types of the image data and found that the type of image degradation is an important factor. Using the F 1 score and the newly proposed sensitivity measure, we found that the parameter sensitivity is not necessarily proportional to the success rate of a method. This also provided an explanation why the best performing method for synthetic data was outperformed by other methods when applied to the real microscopy images. On the basis of the results obtained, we conclude with the recommendation of the HDome method for data with relatively low variations in quality, or the Sorokin method for image sets in which the quality varies more. We also provide alternative recommendations for high-quality images, and for situations in which detailed parameter tuning might be deemed expensive. V C 2015 International Society for Advancement of Cytometry Key terms fluorescence microscopy; 3D imaging; diffraction-limited spot detection; parameter sensitivity RELIABLE spot detection in 3D confocal microscopy is an important task in cell observation. The objects of interest such as proteins or other biomolecules (e.g., sequences of nucleic acids) are typically fluorescently tagged and they appear as bright, diffraction-limited particles in 3D images. For example, the proteins of interest can be genetically modified to become fluorescent while keeping their function (1), or fluorescent antibodies can be attached to proteins (2). The sequences of nucleic acids can be labeled by Fluorescence In-Situ Hybridization (FISH), which allows us to visualize parts of chromosomes such as telomeres or centromeres or even individual genes (3). It is known that the spatial distribution of subcellular objects is related to their function, and thus reliable 3D spot detection is of paramount importance in many biological applications, for example, in particle tracking (4), studies on the spatial arrangement of genes (5-7), in telomere studies (8-11), kinetochore studies (12), and in co-localization studies (13). Reliable spot detection is also essential in the Photoac...

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Automated evaluation of her-2/neu status in breast tissue from fluorescent in situ hybridization images

Cited by 60 publications

References 18 publications

State‐of‐the‐art FISHing: Automated analysis of cytogenetic aberrations in interphase nuclei

State‐of‐the‐art FISHing: Automated analysis of cytogenetic aberrations in interphase nuclei

Iterative h‐minima‐based marker‐controlled watershed for cell nucleus segmentation

Performance and sensitivity evaluation of 3D spot detection methods in confocal microscopy

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