The current subject matter provides a tool for evaluating the risk of failure or the likelihood of success of surgery of healing ligaments and tendons in the body. In some embodiments, a region of a scan comprising one or more of an anterior cruciate ligament (ACL) or an ACL graft can be defined. A magnetic resonance (MR) imaging data set can be obtained. MR parameters characterizing a size and a quality of the ACL or ACL graft can be derived using the MR data. The MR parameters can be used as inputs to a predictive model. A score characterizing a likelihood of failure of the ACL or ACL graft in a human patient can be generated using the predictive model.

A method for processing echocardiography data to enable automatic functional measurements based on cardiac ultrasound images as an input, including (i) classification of the cardiac ultrasound images to ensure that relevant images are passed on to the next steps, optionally utilising a first neural network, such as a convolutional neural network, (ii) segmentation and semantic partitioning of the left ventricle (LV) myocardium to extract relevant parts of the image, optionally by using a second neural network, (iii) regional motion estimates to determine a mapping of displacements in the extracted parts of the image and to output estimated tissue motion vectors for the extracted parts of the image, optionally using a third neural network, and (iv) fusion of measurements via state estimation applied to the tissue motion vectors and thereby incorporating a temporal domain to produce data showing variation of the estimated measurements over time.

The exemplified methods and systems facilitate the training of a noise-robust deep learning network that is sufficiently robust in the recognition of objects in images having extremely noisy elements such that the noise-robust network can match, or exceed, the performance of human counterparts. The extremely noisy elements may correspond to extremely noisy viewing conditions, e.g., that often manifests themselves in the real-world as poor weather or environment conditions, sub-optimal lighting conditions, sub-optimal image acquisition or capture, etc. The noise-robust deep learning network is trained both (i) with noisy training images with low signal-to-combined-signal-and-noise ratio (SSNR) and (ii) either with noiseless, or generally noiseless, training images or a second set of noisy training images having a SSNR value greater than that of the low-SSNR noisy training images.

A virtual satellite system may receive, re-project to a spatial resolution and interpolate to a desired temporal resolution, georeferenced data representing an image of a geographic region from a plurality of different satellites. Bias in the georeferenced data between the plurality of satellites is determined and based on which satellite's image data contains an identified minimum spatial resolution, vegetation index data may be set to one of the satellite's data, which may or may not be adjusted. A target image may be generated based on the set vegetation index data.

An image acquisition unit acquires a CT image and one or more MRI images of the brain of a subject that has developed a cerebral infarction. An infarction region extraction unit extracts an infarction region corresponding to the time elapsed since the development from the MRI image. A registration unit performs registration between the CT image and the MRI image. An infarction region specification unit specifies the infarction region corresponding to the time elapsed since the development in the CT image on the basis of the result of the registration. A learning unit learns a discriminator which discriminates an infarction region corresponding to the time elapsed since the development in the CT image to be discriminated, using the infarction region corresponding to the time elapsed since the development, which has been specified in the CT image, as teacher data.

A computer-implemented method of reducing 4D Digital Subtracted Angiography (DSA) reconstruction artifacts using a computational fluid dynamics (CFD) simulation includes a computer receiving first DSA time sequence data comprising a representation of a plurality of vessels and segmenting a vessel of interest from the first DSA time sequence data. The computer uses the CFD simulation to simulate fluid dynamics across the vessel of interest to yield a flow field and determines a plurality of simulated time activity curve parameters for each voxel inside the vessel of interest using the flow field. Then, the computer applies a reconstruction process to second DSA time sequence data to yield a DSA volume. This reconstruction process is constrained by the plurality of simulated time activity curve parameters for each voxel inside the vessel of interest.

A method for cardiovascular-dynamics correlated imaging includes receiving a time series of images of at least a portion of a patient, receiving a time series of cardiovascular data for the patient, evaluating correlation between the time series of images and the time series of cardiovascular data, and determining a property of the at least a portion of a patient, based upon the correlation. A system for cardiovascular-dynamics correlated imaging includes a processing device having: a processor, a memory communicatively coupled therewith, and a correlation module including machine-readable instructions stored in the memory that, when executed by the processor, perform the function of correlating a time series of images of at least a portion of a patient with a time series of cardiovascular data of the patient to determine a property of the at least a portion of a patient.

One embodiment include an image acquisition circuit that accesses a pre-treatment and a post-treatment image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), a segmentation and registration circuit that annotates the tumor represented in the images, and that registers the pre-treatment image with the post-treatment image; a feature extraction circuit that selects a set of pre-treatment and a set of post-treatment radiomic features from the registered image; a delta radiomics circuit that generates a set of delta radiomic features by computing a difference between the set of post-treatment radiomic features and the set of pre-treatment radiomic features; and a classification circuit that generates a probability that the region of tissue will respond to immunotherapy based on the difference, and that classifies the region of tissue as a responder or non-responder. Embodiments may generate an immunotherapy treatment plan based, at least in part, on the classification.

A method for detecting an indication of likelihood of malignancy, comprising: receiving a sequence of anatomical images of a breast of a target individual acquired over a time interval during which contrast is administered, analyzing the sequence of anatomical images to identify: a baseline pre-contrast image denoting lack of contrast, a peak contrast image denoting a peak contrast enhancement, an initial uptake image denoting initial contrast enhancement, and a delayed response image denoting final contrast enhancement, creating a multi-channel image representation comprising: intensity channel including the peak contrast enhanced image, contrast-update channel including the computed difference between the peak contrast enhanced image and the pre-contrast image, and contrast-washout channel including the computed difference between the initial uptake image and the delayed response image, and computing by a trained deep convolutional neural network, a classification category indicative of likelihood of malignancy for the sequence according to the multi-channel image representation.

Embodiments disclosed herein include devices, systems, and methods for determining tear film break-up time and for detecting eyelid margin contact and blink rates, particularly for diagnosing, measuring, and/or analyzing dry eye conditions and symptoms. The apparatus and methods for determining tear film break-up time and for detecting eyelid margin contact and blink rates, particularly for diagnosing, measuring, and/or analyzing dry eye conditions and symptoms may employ ocular surface interferometry (OSI) devices or other imaging and display devices capable of imaging and displaying a picture of a patient's eye during tear film break-up time and blink rate related procedures.