Workflow and Metrics for Image Quality Control in Large-Scale High-Content Screens

Bray, Mark-Anthony; Fraser, Adam; Hasaka, Thomas; Carpenter, Anne E.

doi:10.1177/1087057111420292

Cited by 99 publications

(107 citation statements)

References 14 publications

(22 reference statements)

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“…In the following we will use the microscopy images from set BBBC006v1 in the Broad Bioimage Benchmark Collection which is described in [14]. The images are available at http://www.broadinstitute.org/bbbc/BBBC006.…”

Section: Images Filters and Distributionsmentioning

confidence: 99%

Generalized extreme value distributions, information geometry and sharpness functions for microscopy images

Lenz

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We introduce the generalized extreme value distributions as descriptors of edge-related visual appearance properties. Theoretically these distributions are characterized by their limiting and stability properties which gives them a role similar to that of the normal distributions. Empirically we will show that these distributions provide a good fit for images from a large database of microscopy images with two visually very different types of images. The generalized extreme value distributions are transformed exponential distributions for which analytical expressions for the Fisher matrix are available. We will show how the determinant of the Fisher matrix and the gradient of the determinant of the Fisher matrix can be used as sharpness functions and a combination of the determinant and the gradient information can be used to improve the quality of the focus estimation.Index Terms-generalized extreme value distribution, information geometry, edge statistics, auto-focus, imagebased screening OVERVIEW AND BACKGROUNDEdge detection is one of the first and most important operations in all technical and biological vision systems. It is therefore important to understand the relation between the statistical properties of the space of input images and the statistical properties of the resulting edge detector values. In this paper we mainly consider images from an automated microscope taking focus series of cells with two different types of staining. We have thus two sets of images with two visually different properties, the optical properties of the system in the form of the focus plane are systematically varied and the optimal focus setting is established with the help of an independent measuring process. Statistical properties of edge detectors have been studied earlier, mainly in the context of natural image statistics (see [1,2,3,4,5,6,7] for some examples). The main observation is that the distributions of these filter results This work was supported by the Swedish Foundation for Strategic Research through grant IIS11-0081 usually have heavy tails, with the (two-parameter) Weibull distribution as the most popular choice. In this paper we will use the generalized extreme value (GEV) distributions which come in three different types: the Fréchet-, (reversed) Weibull-and Gumbel-distributions. We will compare the fitting properties of the GEV distributions with those of the standard Weibull distribution and show that the fitting results for the GEV are comparable or slightly better than those for the Weibull distributions.The second topic we investigate is the usage of concepts from information geometry [8,9] where probability distributions are points on a manifold and methods from differential geometry are used to study the relation between different distributions. Typical concepts used are the distance between distributions or the shortest path (the geodesic) between them. Both, Weibull-and GEV-distributions, can be derived by transformations from the exponential distribution and closed form expressions for the metric ...

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Section: Images Filters and Distributionsmentioning

confidence: 99%

Generalized extreme value distributions, information geometry and sharpness functions for microscopy images

Lenz

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In our experiments we use the microscopy images from set BBBC006v1 in the Broad Bioimage Benchmark Collection which is described in [3], [4]. The images are available at http://www.broadinstitute.org/bbbc/BBBC006.…”

Section: Database and Implementationmentioning

confidence: 99%

“…The sharpness measure based on the determinant of the Fisher information matrix tries to capture this behavior on the Riemannian manifold of the statistical distributions. In the last part of the paper we will investigate the performance of this method with the help of a benchmark dataset of microscopy images described in [3], [4]. We will show that for the focus sequences in the database the method results in a local maximum of the sharpness function in cases where the images contain sufficiently many meaningful objects points.…”

Section: Introductionmentioning

confidence: 99%

Generalized Pareto Distributions—Application to Autofocus in Automated Microscopy

Lenz

2016

IEEE J. Sel. Top. Signal Process.

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Abstract-Dihedral filters correspond to the Fourier transform of functions defined on square grids. For gray value images there are six pairs of dihedral edge-detector pairs on 5x5 windows. In low-level image statistics the Weibull-or the generalized extreme value distributions are often used as statistical distributions modeling such filter results. Since only points with high filter magnitudes are of interest we argue that the generalized Pareto distribution is a better choice. Practically this also leads to more efficient algorithms since only a fraction of the raw filter results have to analyzed. The generalized Pareto distributions with a fixed threshold form a Riemann manifold with the Fisher information matrix as a metric tensor. For the generalized Pareto distributions we compute the determinant of the inverse Fisher information matrix as a function of the shape and scale parameters and show that it is the product of a polynomial in the shape parameter and the squared scale parameter. We then show that this determinant defines a sharpness function that can be used in autofocus algorithms. We evaluate the properties of this sharpness function with the help of a benchmark database of microscopy images with known ground truth focus positions. We show that the method based on this sharpness function results in a focus estimation that is within the given ground truth interval for a vast majority of focal sequences. Cases where it fails are mainly sequences with very poor image quality and sequences with sharp structures in different layers. The analytical structure given by the Riemann geometry of the space of probability density functions can be used to construct more efficient autofocus methods than other methods based on empirical moments.

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“…Recent advances in a diverse consortium of fields from automated sample processing to statistical machine learning, microfluidic-based single-cell analysis [7–9], and high content analysis/screening [10–14] have fueled a renewed interest in quantitative oral cytology. While offering strong potential for enhanced clinical insight relative to early disease detection, the “-omics” data derived from these new capabilities has a tendency to yield putative clinical models that do not perform as well in later validation studies.…”

Section: Introductionmentioning

confidence: 99%

‘Cytology-on-a-chip’ based sensors for monitoring of potentially malignant oral lesions

et al. 2016

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Despite significant advances in surgical procedures and treatment, long-term prognosis for patients with oral cancer remains poor, with survival rates among the lowest of major cancers. Better methods are desperately needed to identify potential malignancies early when treatments are more effective. Objective To develop robust classification models from cytology-on-a-chip measurements that mirror diagnostic performance of gold standard approach involving tissue biopsy. Materials and Methods Measurements were recorded from 714 prospectively recruited patients with suspicious lesions across 6 diagnostic categories (each confirmed by tissue biopsy -histopathology) using a powerful new ‘cytology-on-a-chip’ approach capable of executing high content analysis at a single cell level. Over 200 cellular features related to biomarker expression, nuclear parameters and cellular morphology were recorded per cell. By cataloging an average of 2,000 cells per patient, these efforts resulted in nearly 13 million indexed objects. Results Binary “low-risk”/“high-risk” models yielded AUC values of 0.88 and 0.84 for training and validation models, respectively, with an accompanying difference in sensitivity + specificity of 6.2%. In terms of accuracy, this model accurately predicted the correct diagnosis approximately 70% of the time, compared to the 69% initial agreement rate of the pool of expert pathologists. Key parameters identified in these models included cell circularity, Ki67 and EGFR expression, nuclear-cytoplasmic ratio, nuclear area, and cell area. Conclusions This chip-based approach yields objective data that can be leveraged for diagnosis and management of patients with PMOL as well as uncovering new molecular-level insights behind cytological differences across the OED spectrum.

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Workflow and Metrics for Image Quality Control in Large-Scale High-Content Screens

Cited by 99 publications

References 14 publications

Generalized extreme value distributions, information geometry and sharpness functions for microscopy images

Generalized extreme value distributions, information geometry and sharpness functions for microscopy images

Generalized Pareto Distributions—Application to Autofocus in Automated Microscopy

‘Cytology-on-a-chip’ based sensors for monitoring of potentially malignant oral lesions

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