Embodiments described herein provide for training an artificial intelligence model to become a preference-aware model. The artificial intelligence model preferences as the artificial intelligence model trains. Reinforcement learning is used to train experts in the artificial intelligence model such that each expert is trained to converge to a unique preference. The architecture of the artificial intelligence model is highly flexible. Upon executing a trained model, users can select automatically images according to various preferences based on medical professional preferences, geographic preferences, patient anatomy, and institutional guidelines.

Systems and methods for iteratively computing an image registration or an image segmentation are driven by an optimization function that includes a similarity measure component whose effect on the iterative computations is relatively mitigated based on a monitoring of volume changes of volume elements at image locations during the iterations. A system and a related method quantify a registration error by applying a series of edge detectors to input images and combining related filter responses into a combined response. The series of filters are parameterized with a filter parameter. An extremal value of the combined response is then found and a filter parameter associated with said extremal value is then returned as output. This filter parameter relates to a registration error at a given image location.

Systems and methods for iteratively computing an image registration or an image segmentation are driven by an optimization function that includes a similarity measure component whose effect on the iterative computations is relatively mitigated based on a monitoring of volume changes of volume elements at image locations during the iterations. A system and a related method quantify a registration error by applying a series of edge detectors to input images and combining related filter responses into a combined response. The series of filters are parameterized with a filter parameter. An extremal value of the combined response is then found and a filter parameter associated with said extremal value is then returned as output. This filter parameter relates to a registration error at a given image location.

A method may include identifying a first image for training a deep learning network, wherein the first image includes at least one target object associated with at least one location in the first image, and wherein the first image is associated with a mask image; determining a set of deformations to create a training set of deformed images, wherein the training set is to be used to train the deep learning network; generating the training set of deformed images by applying the set of deformations to the first image; and generating a set of deformed mask images by applying the set of deformations to the mask image, wherein each deformed image of the training set of deformed images is associated with a respective mask image to identify the location of the at least one target object in each deformed image.

A method may include identifying a first image for training a deep learning network, wherein the first image includes at least one target object associated with at least one location in the first image, and wherein the first image is associated with a mask image; determining a set of deformations to create a training set of deformed images, wherein the training set is to be used to train the deep learning network; generating the training set of deformed images by applying the set of deformations to the first image; and generating a set of deformed mask images by applying the set of deformations to the mask image, wherein each deformed image of the training set of deformed images is associated with a respective mask image to identify the location of the at least one target object in each deformed image.

Disclosed is a large-scale crop phenology extraction method based on a shape model fitting method. The method comprises: acquiring a multi-year vegetation index time sequence curve in a localized geographic region; performing smooth fitting on the vegetation index time sequence curve by using a dual logistic function fitting means; establishing shape models by using reference curves and reference points of agrometeorological stations; performing shape model fitting by means of transformation; and obtaining a phenological period extraction value of the localized geographic region by means of calculation using the optimal scaling parameter. According to the present invention, macroscopic features of the curve are used, such that the influence of localized fluctuation and noise of the curve can be reduced, and a better extraction precision is obtained; and each phenological period of a crop can be extracted at the same time.

Disclosed is a large-scale crop phenology extraction method based on a shape model fitting method. The method comprises: acquiring a multi-year vegetation index time sequence curve in a localized geographic region; performing smooth fitting on the vegetation index time sequence curve by using a dual logistic function fitting means; establishing shape models by using reference curves and reference points of agrometeorological stations; performing shape model fitting by means of transformation; and obtaining a phenological period extraction value of the localized geographic region by means of calculation using the optimal scaling parameter. According to the present invention, macroscopic features of the curve are used, such that the influence of localized fluctuation and noise of the curve can be reduced, and a better extraction precision is obtained; and each phenological period of a crop can be extracted at the same time.

A positioning tracking member, a method for recognizing a maker (20), a storage medium, and an electronic device. By directly sticking a positioning tracking member onto the body of a patient, a rigid connection between the positioning tracking member and the human body is not required, thereby avoiding damage to the human body. Furthermore, in combination with a recognition algorithm of the maker (20), recognition of the maker (20) in the image space is quickly achieved by comparing the actual size of each candidate connected region in a three-dimensional medical model with that of the marker (20), the recognition speed being high and the recognition accuracy being high.

A positioning tracking member, a method for recognizing a maker (20), a storage medium, and an electronic device. By directly sticking a positioning tracking member onto the body of a patient, a rigid connection between the positioning tracking member and the human body is not required, thereby avoiding damage to the human body. Furthermore, in combination with a recognition algorithm of the maker (20), recognition of the maker (20) in the image space is quickly achieved by comparing the actual size of each candidate connected region in a three-dimensional medical model with that of the marker (20), the recognition speed being high and the recognition accuracy being high.

A surgical system can be configured to obtain image data of a joint that comprises at least a portion of a humerus; segment the image data to determine a shape for a diaphysis of the humerus; based on the determined shape of the diaphysis, determine an estimated pre-morbid shape of the humerus; based on the estimated shape of the humerus, identify one or more bone fragments in the image data; and based on the identified bone fragments in the image data, generate an output.