Abstract: Methods and systems of analyzing images of a retina captured by an Optical Coherence Tomography (OCT) scanner are disclosed. The methods and systems use a processor configured to implement a series of instructions that include creating a training set of images of a retina of a patient captured by an OCT scanner including labeled regions of diffuse intraretinal fluid (DIRF) such as by assessing maximum and minimum regions of DIRF and regions of pathology of the retina to generate a model, and analyzing the DIRF region or regions of pathology by the model to derive an assessment of the retina of the patient. The assessment of the retina enables, for instance, the treatment of the patient's retina to be evaluated and determined.

Abstract: A computer-implemented method for predicting a blood vessel stenosis is disclosed. The method may include extracting a blood vessel path and its centerline based on the image of the blood vessel. The method may further include determining a candidate stenosis for the blood vessel path and identifying image blocks along the centerline of the blood vessel path within a range of candidate stenosis for the blood vessel path determined based on the candidate stenosis. The method may also include determining a degree of stenosis for the blood vessel path by applying a trained learning network comprising a convolutional neural network and a recurrent neural network on the image blocks within the range of candidate stenosis.

Abstract: The present technology relates to the field of medical monitoring, and, in particular, to non-contact detecting and monitoring of patient breathing. Systems, methods, and computer readable media are described for calculating a change in depth of a region of interest (ROI) on a patient. In some embodiments, the systems, methods, and/or computer readable media can identify steep changes in depths. For example, the systems, methods, and/or computer readable media can identify large, inaccurate changes in depths that can occur at edge regions of a patient. In these and other embodiments, the systems, methods, and/or computer readable media can adjust the identified steep changes in depth before determining one or more patient respiratory parameters.

Abstract: A vehicular lane keeping system includes a forward-viewing camera disposed at a windshield of a vehicle and an electronic control unit (ECU). The ECU is operable to (i) process map data relating to a current geographic location of the vehicle and (ii) process vehicle information relating to current operation of the vehicle in a traffic lane of a road. Responsive to (i) processing of image data captured by said forward-viewing camera and (ii) processing of the map data and (iii) processing of the vehicle information, the vehicular lane keeping system determines a path of travel for the vehicle that keeps the vehicle in the traffic lane during situations where lane markings are not readily determinable. The vehicular lane keeping system determines the path of travel even when no lane markings are present on the road. The vehicular lane keeping system maintains the vehicle along the determined path of travel.

Abstract: A display control unit displays a tomographic image of a specific tomographic plane in a first medical image on a display unit. A finding classification unit classifies each pixel of a partial region of the first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding in the partial region. A weighting coefficient setting unit sets a weighting coefficient indicating a degree of weighting, which varies depending on a size of each finding, for each finding. A similarity derivation unit performs a weighting operation for the first feature amount for each finding calculated in the partial region and a second feature amount for each finding calculated in a second medical image on the basis of the weighting coefficient to derive a similarity between the first medical image and the second medical image.

Abstract: Face anonymization techniques are described that overcome conventional challenges to generate an anonymized face. In one example, a digital object editing system is configured to generate an anonymized face based on a target face and a reference face. As part of this, the digital object editing system employs an encoder as part of machine learning to extract a target encoding of the target face image and a reference encoding of the reference face. The digital object editing system then generates a mixed encoding from the target and reference encodings. The mixed encoding is employed by a machine-learning model of the digital object editing system to generate a mixed face. An object replacement module is used by the digital object editing system to replace the target face in the target digital image with the mixed face.

Abstract: The invention relates, in part, to systems and methods for scoring a sample containing tumor tissue from a cancer patient. The score is representative of a nearness between at least one pair of cells, a first member of the least one pair of cells expressing a first biomarker and a second member of the at least one pair of cells expressing a second biomarker that is different from the first biomarker. The score obtained from these methods can be indicative of a likelihood that a patient may respond positively to immunotherapy.

Abstract: Disclosed in the embodiments of the present disclosure are an image processing method and apparatus. One specific embodiment of the method comprises: acquiring a target image, the target image being an image acquired by photographing content to be corrected; detecting the content to be corrected from the target image to acquire a polygonal frame surrounding the content to be corrected; acquiring a correction result of the content to be corrected, the correction result being used for representing whether the content to be corrected meets preset conditions; generating a correction mark corresponding to the correction result and matching the size of the polygonal frame; and outputting a target image comprising the generated correction mark in order to display the target image comprising the generated correction mark.

Abstract: The present disclosure relates to a method, performed by a processor, for processing a blood vessel image from a blood vessel image, the method comprising the steps of: extracting a target blood vessel from a blood vessel image; determining a region of interest (ROI) in an extraction result of the target blood vessel on the basis of a first input received from a user; and within the determined ROI, identifying an error portion in the extraction result on the basis of a second input received from the user, and correcting the identified error portion.

Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

Abstract: An inspection apparatus includes an image-capturing unit configured to capture an image of an article, an inspection unit configured to inspect the article, a generation-processing unit, and a storage-processing unit. The generation-processing unit generates a data group related to an inspection result obtained by the inspection unit, the data group including article information for distinguishing the article, the inspection result obtained by the inspection unit, captured-image information for distinguishing a captured image captured by the image-capturing unit from another captured image, and a hash value of the captured image that are associated with each other. The storage-processing unit stores the data group and the captured image in respective different storage units.

Abstract: An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

Abstract: Methods and systems are provided for reconstructing images from measurement data using one or more deep neural networks according to a decimation strategy. In one embodiment, a method for reconstructing an image using measurement data comprises, receiving measurement data acquired by an imaging device, selecting a decimation strategy, producing a reconstructed image from the measurement data using the decimation strategy and one or more deep neural networks, and displaying the reconstructed image via a display device. By decimating measurement data to form one or more decimated measurement data arrays, a computational complexity of mapping the measurement data to image data may be reduced from O(N4), where N is the size of the measurement data, to O(M4), where M is the size of an individual decimated measurement data array, wherein M<N.

Abstract: Automated methods and systems are disclosed, including a method comprising: obtaining a first three-dimensional-data point cloud of a horizontal surface of an object of interest, the first three-dimensional-data point cloud having a first resolution and having a three-dimensional location associated with each point in the first three-dimensional-data point cloud; capturing one or more aerial image, at one or more oblique angle, depicting at least a vertical surface of the object of interest; analyzing the one or more aerial image with a computer system to determine three-dimensional locations of additional points on the object of interest; and updating the first three-dimensional-data point cloud with the three-dimensional locations of the additional points on the object of interest to create a second three-dimensional-data point cloud having a second resolution greater than the first resolution of the first three-dimensional-data point cloud.

Abstract: A fingerprint sensor device and a method of making a fingerprint sensor device. As non-limiting examples, various aspects of this disclosure provide various fingerprint sensor devices, and methods of manufacturing thereof, that comprise an interconnection structure, for example a bond wire, at least a portion of which extends into a dielectric layer utilized to mount a plate, and/or that comprise an interconnection structure that extends upward from the semiconductor die at a location that is laterally offset from the plate.

Abstract: In variants, the method for change analysis can include detecting a rare change in a geographic region by comparing a first and second representation, extracted from a first and second geographic region measurement sampled at a first and second time, respectively, using a common-change-agnostic model.

Abstract: A system includes a display, and a database including surgical videos, images of organs in a human body obtained from a medical imaging device, and images of disease in a human body obtained from the medical imaging device. A controller including a processor is coupled to memory, the database, and the display, and the memory stores information that when executed by the processor causes the system to perform operations. For example, the processor may determine first organ information from the images of the organs, and first disease information from the images of the disease. The processor my calculate a similarity score between the first organ information and the first disease information and second disease information and second organ information indexed to the surgical videos. The processor selects one or more of the surgical videos based on the similarity score, and displays the surgical videos on the display.

Abstract: A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator (126) configured to determine a fractional flow reserve value. The system further includes a processor (120) configured to execute the biophysical simulator (126), which employs machine learning to determine the fractional flow reserve value with spectral volumetric image data. The system further includes a display configured to display the determine fractional flow reserve value.

Abstract: Disclosed herein is an apparatus comprising: an X-ray source configured to direct X-rays through a human tissue; an X-ray detector configured to capture an image of the human tissue with the X-rays; wherein the apparatus is configured to identify an image of a blood vessel from the image of the human tissue and configured to determine a blood sugar level based on the image of the blood vessel.

Abstract: A computer visual guidance system for guiding a surgeon through an arthroscopic debridement of a bony pathology, wherein the computer visual guidance system is configured to: (i) receive a 2D image of the bony pathology from a source; (ii) automatically analyze the 2D image so as to determine at least one measurement with respect to the bony pathology; (iii) automatically annotate the 2D image with at least one annotation relating to the at least one measurement determined with respect to the bony pathology so as to create an annotated 2D image; and (iv) display the annotated 2D image to the surgeon so as to guide the surgeon through the arthroscopic debridement of the bony pathology.