An ultrasound image evaluation apparatus includes: an image obtaining section that obtains a first ultrasound image generated based on ultrasounds, and a second ultrasound image generated before the first ultrasound image; an evaluation section that evaluates a distribution of motions at a part in the first ultrasound image, and a corresponding part of tissue in the second ultrasound image; and an output control section that performs control of outputting an evaluation result of the evaluation section.

According to one embodiment, a medical image analyzer includes a retriever, a first artery transition information unit, a vessel pixel selector, a blood vessel transition information unit, an image association unit, and a second artery transition information unit. The retriever retrieves a plurality of time-series images. The first artery transition information unit obtains first artery transition information. The vessel pixel selector selects vessel pixels. The blood vessel transition information unit obtains blood vessel transition information. The image association unit obtains a correspondence relationship between one and another of the time-series images. The second artery transition information unit obtains second artery transition information based on time information, the first artery transition information, and the blood vessel transition information, and the correspondence relationship.

A method and system for tracking blood flow within a vessel of a patient are presented. At least one or more medical images of the patient are acquired showing at least one vessel of the patient. A user marks a proximal point and a distal point on a vessel of interest on the medical images. The vessel of interest is tracked and corrections are made to the tracking using a tracking algorithm. A composite image is generated that encodes time to peak contrast agent intensity at each point of the vessel of interest as well as the intensity of the contrast at that time. A graph of time to peak contrast agent intensity versus distance from a proximal point of the vessel of interest is calculated and displayed to a user.

A system and method for augmenting an endoscopic display during a medical procedure including capturing a real-time image of a working space within a body cavity during a medical procedure. A feature of interest in the image is identified to the system using a user input handle of a surgical robotic system, and a graphical tag is displayed on the image marking the feature.

Systems and methods for assessing expansion of an abnormality are provided. A first input medical image of a patient depicting an abnormality at a first time and a second input medical image of the patient depicting the abnormality at a second time are received. The second input medical image is registered with the first input medical image. The abnormality is segmented from 1) the first input medical image to generate a first segmentation map and 2) the registered second input medical image to generate a second segmentation map. The first segmentation map and the second segmentation map are combined to generate a combined map. Features are extracted from the first input medical image and the registered second input medical image are based on the combined map. Expansion of the abnormality is assessed based on the extracted features using a trained machine learning based network. Results of the assessment are output.

Systems and methods for assessing a disease are provided. Medical imaging data of lungs of a patient is received. The lungs are segmented from the medical imaging data and abnormality regions associated with a disease are segmented from the medical imaging data. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality regions. The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia.

A method and apparatus for implementing scar tissue identification using a processor coupled to a memory is disclosed. The method and apparatus receive a first modality and a second modality. The first modality is of a first type. The second modality is of a second type, which is different from the first type. Each of the first modality and the second modality respectively describe organic tissue of a patient according to the first and second types. The method and apparatus cross reference the first modality and the second modality and generates improved image data for the first modality based on the cross referencing. The image data includes enhanced accuracy over or higher resolution than original data of the first modality.

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.

A method of assessing spinal column stability involves receiving image data corresponding to a spinal column of a patient; determining, based on the image data, a material strength of bony anatomy in at least a portion of the spinal column; completing a first stability assessment of the spinal column, based at least in part on the determined material strength; modifying the image data to simulate removal of bony anatomy or soft tissue from the spinal column to yield modified image data; and completing a second stability assessment of the spinal column, based at least in part on the determined material strength and the modified image data.

The present disclosure relates to a method for predicting biomarker expression from a medical image. The method for predicting biomarker expression includes receiving a medical image, and outputting indices of biomarker expression for the at least one lesion included in the medical image by using a first machine learning model.