Automatic segmentation and supervised learning‐based selection of nuclei in cancer tissue images

Nandy, Kaustav; Gudla, Prabhakar R.; Amundsen, Ryan; Meaburn, Karen J.; Misteli, Tom; Lockett, Stephen

doi:10.1002/cyto.a.22097

Cited by 28 publications

(26 citation statements)

References 60 publications

(81 reference statements)

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“…To determine the spatial position of a gene locus in the cell nucleus, the radial distance of the center of each measured FISH spot from the nuclear border was determined. To eliminate shape and size effects, nuclei were normalized using distance transformation and the normalized radial distance was measured as described in (Nandy et al, 2012) (nucleus periphery = 0; nucleus center = 1). (Figure S1A; see Experimental Procedures).…”

Section: Resultsmentioning

confidence: 99%

Identification of Gene Positioning Factors Using High-Throughput Imaging Mapping

Shachar

Voss

Pegoraro

et al. 2015

Cell

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162

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Summary Genomes are non-randomly arranged in the 3D space of the cell nucleus. Here we have developed HIPMap, a high-precision, high-throughput, automated fluorescent in situ hybridization imaging pipeline, for mapping of the spatial location of genome regions at large scale. High-throughput imaging position mapping (HIPMap) enabled an unbiased siRNA screen for factors involved in genome organization in human cells. We identify 50 cellular factors required for proper positioning of a set of functionally diverse genomic loci. Positioning factors include chromatin remodelers, histone modifiers and nuclear envelope and pore proteins. Components of the replication and post-replication chromatin re-assembly machinery are prominently represented amongst positioning factors and timely progression of cells through replication, but not mitosis, is required for correct gene positioning. Our results establish a method for the large scale mapping of genome locations and have led to the identification of a compendium of cellular factors involved in spatial genome organization.

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Section: Resultsmentioning

confidence: 99%

Identification of Gene Positioning Factors Using High-Throughput Imaging Mapping

Shachar

Voss

Pegoraro

et al. 2015

Cell

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162

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“…We refer the reader to [23] for a detailed explanation of the segmentation methodology. After segmentation, a total of 43,956 candidate nuclei were analyzed.…”

Section: Methodsmentioning

confidence: 99%

Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning

et al. 2012

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BackgroundCorrect segmentation is critical to many applications within automated microscopy image analysis. Despite the availability of advanced segmentation algorithms, variations in cell morphology, sample preparation, and acquisition settings often lead to segmentation errors. This manuscript introduces a ranked-retrieval approach using logistic regression to automate selection of accurately segmented nuclei from a set of candidate segmentations. The methodology is validated on an application of spatial gene repositioning in breast cancer cell nuclei. Gene repositioning is analyzed in patient tissue sections by labeling sequences with fluorescence in situ hybridization (FISH), followed by measurement of the relative position of each gene from the nuclear center to the nuclear periphery. This technique requires hundreds of well-segmented nuclei per sample to achieve statistical significance. Although the tissue samples in this study contain a surplus of available nuclei, automatic identification of the well-segmented subset remains a challenging task.ResultsLogistic regression was applied to features extracted from candidate segmented nuclei, including nuclear shape, texture, context, and gene copy number, in order to rank objects according to the likelihood of being an accurately segmented nucleus. The method was demonstrated on a tissue microarray dataset of 43 breast cancer patients, comprising approximately 40,000 imaged nuclei in which the HES5 and FRA2 genes were labeled with FISH probes. Three trained reviewers independently classified nuclei into three classes of segmentation accuracy. In man vs. machine studies, the automated method outperformed the inter-observer agreement between reviewers, as measured by area under the receiver operating characteristic (ROC) curve. Robustness of gene position measurements to boundary inaccuracies was demonstrated by comparing 1086 manually and automatically segmented nuclei. Pearson correlation coefficients between the gene position measurements were above 0.9 (p < 0.05). A preliminary experiment was conducted to validate the ranked retrieval in a test to detect cancer. Independent manual measurement of gene positions agreed with automatic results in 21 out of 26 statistical comparisons against a pooled normal (benign) gene position distribution.ConclusionsAccurate segmentation is necessary to automate quantitative image analysis for applications such as gene repositioning. However, due to heterogeneity within images and across different applications, no segmentation algorithm provides a satisfactory solution. Automated assessment of segmentations by ranked retrieval is capable of reducing or even eliminating the need to select segmented objects by hand and represents a significant improvement over binary classification. The method can be extended to other high-throughput applications requiring accurate detection of cells or nuclei across a range of biomedical applications.

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“…Authors in [28] adopt the level set method, whereas the segmentation technique of [9] is based on a specific microscopy imaging model. On the other hand, many studies also employ supervised learning-based approaches, such as neural network [15,16] and AAM [14]. Authors in [4] adopt a learning-based template matching approach to segment cell nuclei in microscopy images.…”

Section: Prior Workmentioning

confidence: 98%

Image segmentation of mesenchymal stem cells in diverse culturing conditions

Afridi

Liu

Chan

et al. 2014

IEEE Winter Conference on Applications of Computer Vision

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Researchers in the areas of regenerative medicine and tissue engineering have great interests in understanding the relationship of different sets of culturing conditions and applied mechanical stimuli to the behavior of mesenchymal stem cells (MSCs). However, it is challenging to design a tool to perform automatic cell image analysis due to the diverse morphologies of MSCs. Therefore, as a primary step towards developing the tool, we propose a novel approach for accurate cell image segmentation. We collected three MSC datasets cultured on different surfaces and exposed to diverse mechanical stimuli. By analyzing existing approaches on our data, we choose to substantially extend binarization-based extraction of alignment score (BEAS) approach by extracting novel discriminating features and developing an adaptive threshold estimation model. Experimental results on our data shows our approach is superior to seven conventional techniques. We also define three quantitative measures to analyze the characteristics of images in our datasets. To the best of our knowledge, this is the first study that applied automatic segmentation to live MSC cultured on different surfaces with applied stimuli.

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Automatic segmentation and supervised learning‐based selection of nuclei in cancer tissue images

Cited by 28 publications

References 60 publications

Identification of Gene Positioning Factors Using High-Throughput Imaging Mapping

Identification of Gene Positioning Factors Using High-Throughput Imaging Mapping

Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning

Image segmentation of mesenchymal stem cells in diverse culturing conditions

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