Abstract: A system and method is provided for automated polyp detection in optical colonoscopy images. The system includes an input configured to acquire a series of optical images, and a processor configured to process the optical images. Processing steps include performing a boundary classification with steps comprising locating a series of edge pixels using at least one acquired optical image, selecting an image patch around each said edge pixel, performing a classification threshold analysis on each image patch of said edge pixels using a set of determined boundary classifiers, and identifying, based on the classification threshold analysis, polyp edge pixels consistent with a polyp edge. Processing steps for the processor also include performing a vote accumulation, using the identified polyp edge pixels, to determine a polyp location. The system also includes an output configured to indicate potential polyps using the determined polyp location.

Abstract: A system and methods for polyp detection using optical colonoscopy images are provided. In some aspects, the system includes an input configured to receive a series of optical images, and a processor configured to process the series of optical images with steps comprising of receiving an optical image from the input, constructing an edge map corresponding to the optical image, the edge map comprising a plurality of edge pixel, and generating a refined edge map by applying a classification scheme based on patterns of intensity variation to the plurality of edge pixels in the edge map. The processor may also process the series with steps of identifying polyp candidates using the refined edge map, computing probabilities that identified polyp candidates are polyps, and generating a report, using the computed probabilities, indicating detected polyps. The system also includes an output for displaying the report.

Abstract: A system for quality assessment of optical colonoscopy images includes an input device configured to acquire a series of images during an optical colonoscopy. A computing device is coupled in communication with the input device and configured to acquire from the input device an input image from the series of images captured during the optical colonoscopy; form a cell grid including a plurality of cells on the input image; perform an image transformation onto the input image with each cell of the plurality of cells within the cell grid; reconstruct each cell to form a reconstructed image; compute a difference image of a sum of a plurality of differences between the input image and the reconstructed image; compute a histogram of the difference image; and apply a probabilistic classifier to the histogram to calculate an informativeness score for the input image.

Abstract: Systems, methods, and media for detecting an anatomical object in a medical device image are provided. In some embodiments, system for detecting an anatomical object in a medical device image are provided, the systems comprising: at least one hardware processor that: applies the medical device image to a classifier having a plurality of stages, wherein a first stage of the plurality of stages and a second stage of the plurality of stages each includes a strong learner formed from a plurality of weak learners, and the weak learners in the second stage include a plurality of the weak learners included in the first stage; and identifies the medical device image as being positive or negative of showing the anatomical object based on the application the medical device image to be classifier.

Abstract: A system and method for automating the selection of end-diastolic ultrasound frames (EUFs] and regions of interest (ROIs] of the common carotid artery (CCA] to measure the carotid intima-media thickness (CIMT] is provided. The EUFs are selected based on the QRS complex of the ECG signal associated with an ultrasound video, and the ROI is detected based on image intensity and curvature of the carotid artery bulb. The CIMT and a vascular age of a patient is calculated and displayed on a report.

Abstract: Receiving a plurality of images of a first wall of the patient's heart, the plurality including a first image captured at a time ij and a second image captured at a time t2, determining, based on the plurality of images, a first characteristic of movement of at least a wall portion of the first wall during a time period spanning time t1 and time t2, the first characteristic of movement being determined by using one or more image processing techniques, and the first, characteristic of movement being based on at least one of: (i) a shape of the wall portion in the first image and a shape of the wall portion in the second image, and (ii) a location of the wall portion in the first image and a location of the wall portion in the second image: and outputting an indication of the first characteristic of movement.

Abstract: A system and method for automated polyp detection in optical colonoscopy images is provided. In one embodiment, the system and method for polyp detection is based on an observation that image appearance around polyp boundaries differs from that of other boundaries in colonoscopy images. To reduce vulnerability against misleading objects, the image processing method localizes polyps by detecting polyp boundaries, while filtering out irrelevant boundaries, with a generative-discriminative model. To filter out irrelevant boundaries, a boundary removal mechanism is provided that captures changes in image appearance across polyp boundaries. Thus, in this embodiment the boundary removal mechanism is minimally affected by texture visibility limitations. In addition, a vote accumulation scheme is applied that enables polyp localization from fragmented edge segmentation maps without identification of whole polyp boundaries.

Abstract: A system and method for detecting central pulmonary embolisms in a subject's vasculature is provided. In some aspects, the method includes receiving, using the input, a set of images representing a vasculature of the subject's lungs, automatically analyzing the set of images to segment the main arteries associated with the subject's lungs and separate the main arteries from surrounding tissues. The method also includes automatically extracting central pulmonary embolism candidates from the set of images after segmenting and separating the main arteries, and automatically evaluating the central pulmonary embolism candidates in three-dimensional (3D) space by applying a series of rules. The method further includes automatically displaying a report indicating evaluated central pulmonary embolism candidates on a display.

Abstract: A system and methods are provided for generating intensity modulated proton therapy plans robust to various kinds of delivery uncertainties with the capability for treatment planners to control the balance between plan quality and robustness. The system obtains a representation of a subject and a proton beam configuration that describes a number of beamlets and their respective arrangement. The system computes an objective function, in the treatment planning system, first part of which is about dose deviations from the prescribed dose in the target volumes, second part of which is about dose deviations from the dose constraint in the non-target volumes, and computing dose volume constraints for targets using a probability to control the dose distribution in the target volumes to be between a lower threshold and an upper threshold. Based on this information, the system obtains a robust chance-constrained treatment planning model with a user-adjustable tolerance level.

Abstract: Improved pulmonary embolism (PE) detection may be obtained through computer aided-diagnosis. In particular, PE detection may be accomplished through patient-level diagnosis, embolus-level detection, or a combination of the two. Patient-level diagnosis operates to quickly exclude non-PE patients and dispatch PE-patients to treatment. Embolus-level detection operates to localize individual emboli to support personalized medicine via risk stratification. Multiple instance-based learning (MIBL) classification at the patient level explores the key observation that once any TP candidate of a patient is classified as positive, the patient is identified as PE positive. That is, MIBL focuses on correct classification of patients rather than individual candidates, to effectively and rapidly distinguish between PE patients and non-PE patients.

Abstract: A system and method for detecting pulmonary embolisms in a subject's vasculature are provided. In some aspects, the method includes acquiring a set of images representing a vasculature of the subject, and analyzing the set of images to identify pulmonary embolism candidates associated with the vasculature. The method also includes generating, for identified pulmonary embolism candidates, image patches based on a vessel-aligned image representation, and applying a set of convolutional neural networks to the generated image patches to identify pulmonary embolisms. The method further includes generating a report indicating identified pulmonary embolisms.

Abstract: Methods, systems, and media for generating and/or analyzing medical images having elongated structures are provided. In some embodiments, a method for analyzing medical images is provided, the method comprising: receiving a plurality of images, wherein each of the plurality of images includes a portion of an elongated structure; receiving a location of interest within a least one of the plurality of images; determining an orientation of the elongated structure in response to receiving the location of interest; adjusting image planes of each of the plurality of images to correspond with the orientation of the elongated structure; and causing the elongated structure to be displayed in the adjusted image planes.

Abstract: A system and method is provided for automated polyp detection in optical colonoscopy images. The system includes an input configured to acquire a series of optical images, and a processor configured to process the optical images. Processing steps include performing a boundary classification with steps comprising locating a series of edge pixels using at least one acquired optical image, selecting an image patch around each said edge pixel, performing a classification threshold analysis on each image patch of said edge pixels using a set of determined boundary classifiers, and identifying, based on the classification threshold analysis, polyp edge pixels consistent with a polyp edge. Processing steps for the processor also include performing a vote accumulation, using the identified polyp edge pixels, to determine a polyp location. The system also includes an output configured to indicate potential polyps using the determined polyp location.

Abstract: Systems, methods, and media for on-line boosting of a classifier are provided, comprising: receiving a training sample; for each of a plurality of features, determining a feature value for the training sample and the feature, using the feature value to update a histogram, and determining a threshold for a classifier of the feature; for each of the plurality of features, classifying the training sample using the threshold for the classifier of the feature and calculating an error associated with the classifier; selecting a plurality of best classifiers from the classifiers; and, for each of the plurality of best classifiers, assigning a voting weight to the one of the plurality of best classifiers.

Abstract: A system and methods for polyp detection using optical colonoscopy images are provided. In some aspects, the system includes an input configured to receive a series of optical images, and a processor configured to process the series of optical images with steps comprising of receiving an optical image from the input, constructing an edge map corresponding to the optical image, the edge map comprising a plurality of edge pixel, and generating a refined edge map by applying a classification scheme based on patterns of intensity variation to the plurality of edge pixels in the edge map. The processor may also process the series with steps of identifying polyp candidates using the refined edge map, computing probabilities that identified polyp candidates are polyps, and generating a report, using the computed probabilities, indicating detected polyps. The system also includes an output for displaying the report.

Abstract: Systems, methods, and media for updating a classifier are provided, in some embodiments, systems for updating a classifier are provided, the systems comprising: a hardware processor that is configured to: receive a sample; for each of a first plurality of weak learners, classify the sample using the weak learner, determine an outcome of the classification, and determine an up-dated error rate of the weak learner based on the outcome of the classification and at least one of: (i) a count of positive samples used to update the classifier, and (ii) a count of negative samples used to update the classifier; select a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and update tire classifier based on the first weak learner.

Abstract: Receiving a plurality of images of a first wall of the patient's heart, the plurality including a first image captured at a time ij and a second image captured at a time t2, determining based on the plurality of images, a first characteristic of movement of at least a wall portion of the first wall during a time period spanning time t1 and time t2, the first characteristic of movement being determined by using one or more image processing techniques, and the first, characteristic of movement being based on at least one of: (i) a shape of the wall portion in the first image and a shape of the wall portion in the second image, and (ii) a location of the wall portion in the first image and a location of the wall portion in the second image: and outputting an indication of the first characteristic of movement.

Abstract: Systems, methods, and media for updating a classifier are provided, in some embodiments, systems for updating a classifier are provided, the systems comprising: a hardware processor that is configured to: receive a sample; for each of a first plurality of weak learners, classify the sample using the weak learner, determine an outcome of the classification, and determine an up-dated error rate of the weak learner based on the outcome of the classification and at least one of: (i) a count of positive samples used to update the classifier, and (ii) a count of negative samples used to update the classifier; select a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and update tire classifier based on the first weak learner.

Abstract: Hair conditioning composition comprising: —from 0.1 to 10% wt. silicone —from 0.1 to 5% wt. metal pyrithione blended with non-anionic surfactant.

Abstract: Systems, methods, and media for detecting an anatomical object in a medical device image are provided. In some embodiments, system for detecting an anatomical object in a medical device image are provided, the systems comprising: at least one hardware processor that: applies the medical device image to a classifier having a plurality of stages, wherein a first stage of the plurality of stages and a second stage of the plurality of stages each includes a strong learner formed fro ma plurality of weak learners, and the weak learners in the second stage include a plurality of the weak learners included in the first stage; and identifies the medical device image as being positive or negative of showing the anatomical object based on the application the medical device image to be classifier.