Abstract: Systems and methods for image processing are provided in the present disclosure. The systems may generate a preliminary image by filtering image data generated by an image acquisition device. The system may generate an intermediate image by performing, based on a first objective function, a first iterative operation on the preliminary image. The first objective function may include a first term associated with a first difference between the intermediate image and the preliminary image, a second term associated with continuity of the intermediate image and a third term associated with sparsity of the intermediate image. The systems may also generate a target image by performing, based on a second objective function, a second iterative operation on the intermediate image. The second objective function may be associated with a system matrix of the image acquisition device and a second difference between the intermediate image and the target image.

Abstract: Methods and systems for insertion of an instrument into a body cavity of an animal for performing a surgical procedure using a processor circuit controlled robotic surgery system are disclosed. In some embodiments, a method involves receiving body cavity image data representing an interior view of the body cavity captured by a camera inserted into the body cavity, determining, by the processor circuit, instrument parameters associated with physical extents of the instrument to be inserted, determining, by the processor circuit, an instrument envelope identifying a region through which the instrument is capable of moving in the body cavity, and generating, by the processor circuit, display signals operable to display a composite view of the interior of the body cavity on a display, the composite view being based on the body cavity image data and including an envelope overlay image generated to represent the instrument envelope.

Abstract: A method of qualifying physical components using computed tomography (CT) includes obtaining qualified CT data from a CT scanner for at least one qualified physical component. Qualification data is generated based on the qualified CT data, where the qualification data defines a qualification envelope. Candidate CT data is obtained from the CT scanner for a candidate physical component. Comparison data is then generated based on the candidate CT data and the qualification data, where the comparison data indicates whether the candidate CT data is within the qualification envelope defined by the qualification data. An acceptance signal is generated if the comparison data meets acceptance criteria.

Abstract: The disclosure relates to systems and methods for magnetic resonance imaging (MRI). A method may include obtaining k-space data associated with MR signals acquired by an MR scanner. The k-space data may corresponding to a first sampling rate. The method may also include generating one or more estimated images based on the k-space data and a target neural network model. The one or more estimated images may correspond to a second sampling rate that exceeds the first sampling rate. The method may further include determining one or more target images based on the one or more estimated images and the k-space data using a compressed sensing model. The compressed sensing model may be constructed based on the one or more estimated images.

Abstract: A method and system perform single phase and multi-phase contour refinement of lesions. The method includes receiving a three dimensional input mask; receiving input slices from the medical images including a lesion; cropping the input slices with the input mask; performing lesion contour refinement for the cropped input slices and the input mask to obtain a predicted mask; and storing the predicted mask that includes 3D lesion contour refinement. A multiphase method includes deforming the 3D input mask from the reference phase to a target phase or warping the input slices from the target phase to the reference phase before contour refinement. The warped images generate an output mask in the reference phase coordinate system that is then deformed to the target phase coordinate system for display.

Abstract: Systems and methods for automatically populating a post-operative report of a surgical procedure are disclosed. A system may include at least one processor configured to implement a method including receiving an identifier of a patient, an identifier of a healthcare provider, and surgical footage of a surgical procedure performed on the patient. The method may include analyzing frames of the surgical footage to identify phases of the surgical procedure based on interactions between medical instruments and biological structures and, based on the interactions, associate a name with each phase. The method may include determining a beginning of each phase and associating a time marker with the beginning of each phase. The method may include populating a post-operative report with the patient identifier, the names of the phases, and time markers associated with the phases in a manner that enables the health care provider to alter the post-operative report.

Abstract: Systems and methods for predicting a risk of cardiovascular disease (CVD) from one or more fundus images are disclosed. Fundus images associated with an individual are processed to determine whether fundus images are of sufficient quality. The fundus images of sufficient quality are processed to identify fundus images belonging to a single eye. A plurality of risk contributing factor sets of CNNs (RCF CNN) are configured to output an indicator of probability of the presence of a different risk contributing factor in each of the one or more fundus images. At least one of the RCF CNNs is configured in a jury system model having a plurality of jury member CNNs, each being configured to output a probability of a different feature in the one or more fundus images. The outputs of the jury member CNNs are processed to determine the indicator of probability of the presence of the risk contributing factor output by the RCF CNN.

Abstract: A method for checking a dissection process in a laser microdissection system includes carrying out the dissection process for cutting out a dissectate from an object in a first region of the object by a laser beam. First image data is acquired of at least the first region of the object after the dissection process. It is examined whether the first image data has sharp structures within a region to be separated by the dissection process in order to determine whether the dissection process was successful.

Abstract: A computer-implemented method of processing complex magnetic resonance (MR) images is provided. The method includes receiving a pair of corrupted complex data and pristine complex images. The method also includes training a neural network model using the pair by inputting the corrupted complex data to the neural network model, setting the pristine complex images as target outputs, and processing the corrupted complex data using the neural network model to derive output complex images of the corrupted complex data. Training a neural network model also includes comparing the output complex images with the target outputs by computing a phase-sensitive structural similarity index measure (PS-SSIM) between each of the output complex images and its corresponding target complex image, wherein the PS-SSIM is real-valued and varies with phases of the output complex image and phases of the target complex image, and adjusting the neural network model based on the comparison.

Abstract: A computer-implemented method is for classifying a lesion. In an embodiment, the method includes receiving a first medical image of an examination volume, the first medical image corresponding to a first examination time; receiving a second medical image of the examination volume, the second medical image corresponding to a second examination time, different from the first examination time; determining a first lesion area corresponding to a lesion within the first medical image; determining a registration function based on a comparison of the first medical image and the second medical image; determining a second lesion area within the second medical image based on the registration function and the first lesion area; and classifying the lesion within the first medical image based on the second lesion area. A computer-implemented method for providing a trained classification function, a classification system, and computer program products and computer-readable media are also disclosed.

Abstract: The present disclosure proposes an apparatus for determining a bone age. The apparatus may divide an input image capturing a human body into a plurality of segmented images, determine a first segmented image having a highest priority for a first body part from the segmented images, process each of first pixels of the first segmented image based on a reference value, select a first reference image for the first body part from a reference image set, determine whether or not a partial region matching the first reference image exists in the first segmented image processed by the reference value, upon determining that the partial region exists, determine a bone age grade of the first body part based on the first reference image, and determine a bone age of the human body based on the bone age.

Abstract: An image processing method is configured to input a test image to a first learning model, input an output image to a second learning model and output an output image as a result image in which the position of the detected part is indicated by a representative point. The first learning model is constructed by deep learning using teacher data associating a first image in which a marker is expressed and a second image in which the marker is not expressed. The first and the second images are captured to include a same cell. The second learning model is constructed by deep learning using teacher data associating a third image, which is captured to include a cell and in which the marker is expressed, and information representing a position of the representative point included in the third image.

Abstract: The present disclosure deals with the quickening of MRI examinations. Subjects of the present disclosure are a method, a system, a computer program product, a use, a contrast agent for use and a kit.

Abstract: The systems and methods can accurately and efficiently determine a myocardial risk from a lesion disposed along a coronary segment using hemodynamic characteristic(s) associated with one or more sections of the corresponding lesion site. The method may include segmenting one or more lesion sites disposed along at least one arterial segment of the one or more arterial segments of the coronary model into one or more sections. Each lesion site includes a lesion. The method may include determining one or more characteristics for at least one section using at least the one or more characteristics associated with the at least one arterial segment. The one or more characteristics for the at least one section including hemodynamic force characteristic(s) (e.g., wall shear stress (WSS)). The method may include determining one or more risk indices for each lesion site using at least the hemodynamic force characteristic(s) for the at least one section.

Abstract: Disclosed herein are platforms, systems, and methods including a cell culture system that includes a cell culture container comprising a cell culture, the cell culture receiving input cells, a cell imaging subsystem configured to acquire images of the cell culture, a computing subsystem configured to perform a cell culture process on the cell culture according to the images acquired by the cell imaging subsystem, and a cell editing subsystem configured to edit the cell culture to produce output cell products according to the cell culture process.

Abstract: A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.

Abstract: The current disclosure provides for mapping medical images to style transferred medical images using deep neural networks, while maintaining clinical quality of the style transferred medical image, thereby enabling a clinician to evaluate medical images in a preferred style without loss of clinically relevant content. In one embodiment the current disclosure provides for a method comprising, acquiring a medical image of an anatomical region of a subject, wherein the medical image is in a first style, selecting a target style, wherein the target style is distinct from the first style, selecting a clinical quality metric, selecting a trained style transfer network based on the target style and the clinical quality metric, mapping the medical image to a style transferred medical image using the trained style transfer network, wherein the style transferred medical image is in the target style, and displaying the style transferred medical image via a display device.

Abstract: Various embodiments are directed to video-based deep learning evaluation of cardiac ultrasound that accurately identify cardiomyopathy and predict ejection fraction, the most common metric of cardiac function. Embodiments include systems and methods for analyzing images obtained from an echocardiogram. Certain embodiments include receiving video from a cardiac ultrasound of a patient illustrating at least one view the patient's heart, segmenting a left ventricle in the video, and estimating ejection fraction of the heart. Certain embodiments include at least one machine learning algorithm.

Abstract: Methods and systems for providing various skin-related metrics and tracking the effects of skincare and cosmetic products are described. The system may acquire user images via optical scanning methods and analyze the images before and after application of a product to provide quantitative feedback to the user of beneficial or adverse effects of the product. The system may track response of the skin based on changes in inflammation, dryness, elasticity, pH levels, and/or microbiomes and correlate these changes with user information including ethnicity, location, and lifestyle to generate models that are capable of predicting a user's response to certain ingredients and/or predicting long-tern effects of certain ingredients on the skin.

Abstract: Disclosed herein are platforms, systems, and methods including a cell culture system that includes a cell culture container comprising a cell culture, the cell culture receiving input cells, a cell imaging subsystem configured to acquire images of the cell culture, a computing subsystem configured to perform a cell culture process on the cell culture according to the images acquired by the cell imaging subsystem, and a cell editing subsystem configured to edit the cell culture to produce output cell products according to the cell culture process.