A system or method according to at least one embodiment of the present disclosure includes receiving a preoperative image of a patient in a first posture; receiving an intraoperative image of the patient in a second posture; comparing the preoperative image of the patient in the first posture with the intraoperative image of the patient in the second posture; and determining, based on comparing the preoperative image of the patient in the first posture with the intraoperative image of the patient in the second posture, a difference between the first posture and the second posture, the adequate surgical changes are imparted to the second posture to achieve satisfactory surgical outcome.

The present application relates to a method for acquiring maximum principal strain or a maximum principal stress status of a vessel wall. The method includes: acquiring first vessel data of a first time phase corresponding to a vessel; acquiring second vessel data of a second time phase corresponding to the vessel; generating, based on the first vessel data, a first vessel model relating to the first time phase, generating a second vessel model relating to the second time phase based on the second vessel data; determining a region of interest in the first vessel model; determining the corresponding region of interest in the second vessel model; determining a reference point in the region of interest of the first vessel model; determining the corresponding reference point in the region of interest of the second vessel model; determining a displacement of the reference point from the first vessel model to the second vessel model; and determining a maximum principal strain or a maximum principal stress at the reference point based on the displacement of the reference point.

Systems and methods for integrating a three-dimensional X-ray computed tomography system with an independent sound wave system to determine mechanical properties of tissue using signals from the sound wave system. Methods are disclosed that generate a numerical simulation and take the transmitted wave signals as the optimization objective to estimate modulus distribution of the tissue. Further, the mechanical properties of the tissue are reconstructed based on an inverse algorithm.

A system and method for animal disease management is disclosed. The method includes receiving one or more images, one or more videos of one or more animals or a combination thereof and identifying one or more faces of the one or more animals in the one or more images, the one or more videos of one or more animals or a combination thereof. The method further includes extracting one or more facial features and one more muzzle features from the one or more faces and determining one or more facial changes and one or more muzzle changes. The method includes detecting presence or absence of one or more diseases in the one or more animals, predicting likelihood of the one or more diseases or a combination thereof based on the one or more facial changes, the one or more muzzle changes and the predefined information.

A system and method for guiding invasive medical devices relative to other medical devices is disclosed. An imaging device can generate images of the invasive medical device within a body. A trained model for the invasive medical device can be trained on annotated images of the invasive medical device with at least one of orientation and position information. An imaging computer system can apply the trained model to unannotated images of the invasive medical device to determine at least one of a current orientation and a current position of the invasive medical device relative to another medical device within the body. At least one of visual orientation information representing the current orientation of the invasive medical device relative to the other medical device and visual position information representing the current position of the invasive medical device relative to the other medical device within the body can be outputted.

There is provided an information processing apparatus, including: an image obtaining unit configured to obtain a plurality of images of a fertile ovum captured in time series; a recognizing unit including a probability image generating unit configured to generate, for each image of the fertile ovum, a probability image, wherein each position in the probability image represents the probability that at least part of the fertile ovum is present at the corresponding position in the image of the fertile ovum; and a feature amount calculating unit configured to calculate time-series transformation of the fertile ovum from the probability images over the time series, and calculate a feature amount of the fertile ovum based on the transformation.

Digital imaging and learning systems and methods are described for analyzing pixel data of a scalp region of a user's scalp to generate one or more user-specific scalp classifications. A digital image of a user is received at an imaging application (app) and comprises pixel data of at least a portion of a scalp region of the user's scalp. A scalp based learning model, trained with pixel data of a plurality of training images depicting scalp regions of scalps of respective individuals, analyzes the image to determine at least one image classification of the user's scalp region. The imaging app generates, based on the at least one image classification, a user-specific scalp classification designed to address at least one feature identifiable within the pixel data comprising the at least the portion of a scalp region of the user's scalp.

A method for calculating a heart parameter includes receiving a series of two-dimensional images of a heart, the series covering at least one heart cycle. The method includes calculating a volume of the heart in a first systole image based on an orientation of the heart in the first systole image and a segmentation of the heart in the first systole image, and a volume of the heart in a first diastole image based at least on an orientation of the heart in the first diastole image and a segmentation of the heart in the first diastole image; determining the heart parameter based at least on the volume of the heart in the first systole image and the volume of the heart in the first diastole image; determining a confidence score of the heart parameter; and displaying the heart parameter and the confidence score.

Ultrasound imaging systems for automatically identifying and saving ultrasound images relevant to a needle injection procedure, and associated systems and methods, are described herein. For example, an ultrasound imaging system includes a transducer for transmitting/receiving ultrasound signals during a needle injection procedure, and receive circuitry configured to convert the received ultrasound signals into ultrasound image data. The image data can be stored in a buffer memory. A processor can analyze the image data stored in the buffer memory to identify image data that depicts a specified injection event of the needle injection procedure, and the identified image data can be stored in a memory for archival purposes.

A method may include identifying a simulated three-dimensional representation corresponding to an internal anatomy of a subject based on a match between a computed two-dimensional image corresponding to the simulated three-dimensional representation and a two-dimensional image depicting the internal anatomy of the subject. Simulations of the electrical activities measured by a recording device with standard lead placement and nonstandard lead placement may be computed based on the simulated three-dimensional representation. A clinical electrogram and/or a clinical vectorgram for the subject may be corrected based on a difference between the simulations of electrical activities to account for deviations arising from patient-specific lead placement as well as variations in subject anatomy and pathophysiology.