Abstract: Devices, systems, and methods obtain one or more training images; obtain a test image; select one or more associated pixels in the training images for a target pixel in the training images; calculate respective value relationships between a value of the target pixel and respective values of the associated pixels in the training images; select one or more associated pixels in the test image for a target pixel in the test image; and detect an anomaly in the target pixel in the test image based on the respective value relationships between the value of the target pixel and the respective values of the associated pixels in the training images and on respective value relationships between a value of the target pixel and respective values of the associated pixels in the test image.

Abstract: Some devices, systems, and methods obtain training images; select a first reference image and a second reference image from the training images; generate a first set of aligned training images, wherein generating the first set of aligned training images includes aligning the training images to the first reference image; generate a first anomaly-detection model based on the first set of aligned training images; generate a second set of aligned training images, wherein generating the second set of aligned training images includes aligning the training images to the second reference image; and generate a second anomaly-detection model based on the second set of aligned training images.

Abstract: Devices, systems, and methods obtain one or more training images; obtain a test image; select one or more associated pixels in the training images for a target pixel in the training images; calculate respective value relationships between a value of the target pixel and respective values of the associated pixels in the training images; select one or more associated pixels in the test image for a target pixel in the test image; and detect an anomaly in the target pixel in the test image based on the respective value relationships between the value of the target pixel and the respective values of the associated pixels in the training images and on respective value relationships between a value of the target pixel and respective values of the associated pixels in the test image.

Abstract: Some embodiments of devices, systems, and methods obtain at least one first image, wherein the at least one first image is defined in an image space; select at least one feature in the at least one first image; define a topology based on the at least one feature; generate a topology mapping between the topology and the image-space topology; obtain a plurality of anomaly scores, wherein each anomaly score of the plurality of anomaly scores was generated based on a respective detection area in a second image; map the plurality of anomaly scores to the topology based on the topology mapping; and normalize each anomaly score in the plurality of anomaly scores based on the respective neighboring anomaly scores in the topology, thereby generating normalized anomaly scores.

Abstract: The present invention relates to a method and system for determining Copy Number Variations (CNVs) in a genomic test sample including target amplicons and a reference amplicons. Specifically, nucleic acid melting curves are generated for the test sample. A mathematical model is fitted to each of the nucleic acid melting curves to separate target and reference melting reactions within the measured nucleic acid melting curve. The fitting parameters of the mathematical model are calculated. A CNV of the test sample is determined based on the fitting parameters of the mathematical model corresponding to the target and reference melting reactions.

Abstract: Some embodiments of devices, systems, and methods obtain at least one first image, wherein the at least one first image is defined in an image space; select at least one feature in the at least one first image; define a topology based on the at least one feature; generate a topology mapping between the topology and the image-space topology; obtain a plurality of anomaly scores, wherein each anomaly score of the plurality of anomaly scores was generated based on a respective detection area in a second image; map the plurality of anomaly scores to the topology based on the topology mapping; and normalize each anomaly score in the plurality of anomaly scores based on the respective neighboring anomaly scores in the topology, thereby generating normalized anomaly scores.

Abstract: Devices, systems, and methods obtain an image; perform at least one nonlinear locally-adaptive mapping on the image, thereby generating a revised image; generate an edge-enhanced image based on the revised image; and perform dynamic contrast stretching on the edge-enhanced image, thereby generating an enhanced image.

Abstract: An apparatus for automatically adjusting a collection of images based on their lighting conditions is provided. The apparatus obtains one or more images, determines a first lighting condition scores for each lighting condition and for each of the one or more images using a trained prediction model, and labels the each of the one or more images based on the determined first lighting condition scores.

Abstract: The present invention relates to methods for the analysis of nucleic acids present in biological samples, and more specifically to normalize a high resolution melt curve to assist in the identification of one or more properties of the nucleic acids. The present invention provides methods and systems that incorporate a background identification algorithm according to invention principles using raw melt curve data to identify reactions that are unrelated actual DNA melt reactions. Furthermore, a web-based application for analyzing experimental data is provided. The raw experimental data obtained from a variety of instruments is processed and analyzed on a server and presented to a user through a user interface (UI).

Abstract: Some embodiments of systems, devices, and methods obtain a plurality of training images; obtain one or more target regions; generate one or more respective statistical characterizations of the one or more target regions; obtain a test image; locate the one or more target regions on the test image; compare the one or more target regions in the test image to the one or more respective statistical characterizations of the one or more target regions; and detect anomalies in the one or more target regions in the test image based at least in part on the comparison of the one or more target regions in the test image to the one or more respective statistical characterizations of the one or more target regions.

Abstract: Devices, systems, and methods obtain a reference image; obtain a test image; globally align the test image to the reference image; select subfields in the test image; align the subfields in the test image with respective areas in the reference image; warp the test image based on the aligning of the subfields; select anchor points in the reference image; select anchor-edge points in the reference image; realign the subfields in the warped test image with respective areas in the reference image based on the anchor points in the reference image and on the anchor-edge points in the reference image; and warp the warped test image based on the realigning of the subfields.

Abstract: Devices, systems, and methods obtain a reference image; obtain a test image; globally align the test image to the reference image; select subfields in the test image; align the subfields in the test image with respective areas in the reference image; warp the test image based on the aligning of the subfields; select anchor points in the reference image; select anchor-edge points in the reference image; realign the subfields in the warped test image with respective areas in the reference image based on the anchor points in the reference image and on the anchor-edge points in the reference image; and warp the warped test image based on the realigning of the subfields.

Abstract: The present invention relates to methods and systems for the analysis of nucleic acids present in biological samples, and more specifically, relates to clustering melt curves derived from high resolution thermal melt analysis performed on a sample of nucleic acids, the resulting clusters being usable, in one embodiment, for analyzing the sequences of nucleic acids and to classify their genotypes that are useful for determining the identity of the genotype of a nucleic acid that is present in a biological sample.

Abstract: Devices, systems, and methods obtain a reference image; obtain a test image; globally align the test image to the reference image; select subfields in the test image; align the subfields in the test image with respective areas in the reference image; warp the test image based on the aligning of the subfields; select anchor points in the reference image; select anchor-edge points in the reference image; realign the subfields in the warped test image with respective areas in the reference image based on the anchor points in the reference image and on the anchor-edge points in the reference image; and warp the warped test image based on the realigning of the subfields.

Abstract: Devices, systems, and methods obtain training images; generate image-alignment data based on the training images; cluster the training images based at least in part on the image-alignment data, thereby generating clusters of training images; and select one or more representative images from the training images based on the clusters of training images.

Abstract: Devices, systems, and methods obtain a reference image; obtain a test image; globally align the test image to the reference image; select subfields in the test image; align the subfields in the test image with respective areas in the reference image; warp the test image based on the aligning of the subfields; select anchor points in the reference image; select anchor-edge points in the reference image; realign the subfields in the warped test image with respective areas in the reference image based on the anchor points in the reference image and on the anchor-edge points in the reference image; and warp the warped test image based on the realigning of the subfields.

Abstract: Devices, systems, and methods obtain a reference image; obtain a test image; globally align the test image to the reference image; select subfields in the test image; align the subfields in the test image with respective areas in the reference image; warp the test image based on the aligning of the subfields; select anchor points in the reference image; select anchor-edge points in the reference image; realign the subfields in the warped test image with respective areas in the reference image based on the anchor points in the reference image and on the anchor-edge points in the reference image; and warp the warped test image based on the realigning of the subfields.

Abstract: Some embodiments of systems, devices, and methods obtain a plurality of training images; obtain one or more target regions; generate one or more respective statistical characterizations of the one or more target regions; obtain a test image; locate the one or more target regions on the test image; compare the one or more target regions in the test image to the one or more respective statistical characterizations of the one or more target regions; and detect anomalies in the one or more target regions in the test image based at least in part on the comparison of the one or more target regions in the test image to the one or more respective statistical characterizations of the one or more target regions.

Abstract: Some embodiments of devices, systems, and methods obtain at least one first image, wherein the at least one first image is defined in an image space; select at least one feature in the at least one first image; define a topology based on the at least one feature; generate a topology mapping between the topology and the image-space topology; obtain a plurality of anomaly scores, wherein each anomaly score of the plurality of anomaly scores was generated based on a respective detection area in a second image; map the plurality of anomaly scores to the topology based on the topology mapping; and normalize each anomaly score in the plurality of anomaly scores based on the respective neighboring anomaly scores in the topology, thereby generating normalized anomaly scores.

Abstract: Devices, systems, and methods obtain respective corresponding feature-detection scores for a plurality of areas in an image; calculate respective corresponding sorting scores for at least some areas of the plurality of areas; for the at least some areas of the plurality of areas, arrange the corresponding feature-detection scores in order of the corresponding sorting scores, thereby generating an order of sorted feature-detection scores; and assign respective detection scores to the at least some areas based on the order of sorted feature-detection scores and on three or more of the following: the respective corresponding feature-detection scores of the areas, a spectral threshold, a spatial threshold, and a neighborhood kernel.