A blood vessel detecting apparatus and an image-based blood vessel detecting method are provided. In the method, first to-be-evaluated data is detected through a first detecting model to obtain a first detection result. Second to-be-evaluated data is detected through a second detecting model to obtain a second detection result. The first to-be-evaluated data includes one or more medical images obtained from photographing a blood vessel. The first detection result output by the first detecting model includes one or more pixels in the medical image belonging to the blood vessel. The first detecting model and the second detecting model are constructed based on a machine learning algorithm. The second to-be-evaluated data includes the first detection result. The second detection result output by the second detecting model includes one or more pixels in the medical image belonging to the blood vessel.

A method for generating a representation of a three-dimensional protective device includes accessing a scan of anatomical data of a target and identifying a reference model of a closest size or proportion to a size or proportion of the target. The method further includes creating a boundary of a three-dimensional protective device using the reference model and the scan of anatomical data of the target. Additionally, the method includes generating a representation of a continuous, three-dimensional surface of the three-dimensional protective device that corresponds to the scan of anatomical data and the reference model within the boundary of the three-dimensional protective device.

A method for generating a representation of a three-dimensional protective device includes accessing a scan of anatomical data of a target and identifying a reference model of a closest size or proportion to a size or proportion of the target. The method further includes creating a boundary of a three-dimensional protective device using the reference model and the scan of anatomical data of the target. Additionally, the method includes generating a representation of a continuous, three-dimensional surface of the three-dimensional protective device that corresponds to the scan of anatomical data and the reference model within the boundary of the three-dimensional protective device.

The present invention discloses a method for automatic segmentation of a fuzzy boundary image based on active contour and deep learning. In the method, firstly, a fuzzy boundary image is segmented using a deep convolutional neural network model to obtain an initial segmentation result; then, a contour of a region inside the image segmented using the deep convolutional neural network model is used as an initialized contour and a contour constraint of an active contour model; and the active contour model drives, through image characteristics of a surrounding region of each contour point, the contour to move towards a target edge to derive an accurate segmentation line between a target region and other background regions. The present invention introduces an active contour model on the basis of a deep convolutional neural network model to further refine a segmentation result of a fuzzy boundary image, which has the capability of segmenting a fuzzy boundary in the image, thus further improving the accuracy of segmentation of the fuzzy boundary image.

The present invention discloses a method for automatic segmentation of a fuzzy boundary image based on active contour and deep learning. In the method, firstly, a fuzzy boundary image is segmented using a deep convolutional neural network model to obtain an initial segmentation result; then, a contour of a region inside the image segmented using the deep convolutional neural network model is used as an initialized contour and a contour constraint of an active contour model; and the active contour model drives, through image characteristics of a surrounding region of each contour point, the contour to move towards a target edge to derive an accurate segmentation line between a target region and other background regions. The present invention introduces an active contour model on the basis of a deep convolutional neural network model to further refine a segmentation result of a fuzzy boundary image, which has the capability of segmenting a fuzzy boundary in the image, thus further improving the accuracy of segmentation of the fuzzy boundary image.

A method for determining hemodynamic parameters may be provided. The method may include obtaining image data of a subject. The method may include generating a first vascular model and a second vascular model based on the image data and coupling the first vascular model with the second vascular model using an intermediate model to form a coupled vascular model. The method may also include setting at least one of a first boundary condition of the first vascular model or a second boundary condition of the second vascular model and determining a flow field distribution of the coupled vascular model based on the at least one of the first boundary condition or the second boundary condition. The method may further include determining hemodynamic parameters based on the flow field distribution.

A method for determining hemodynamic parameters may be provided. The method may include obtaining image data of a subject. The method may include generating a first vascular model and a second vascular model based on the image data and coupling the first vascular model with the second vascular model using an intermediate model to form a coupled vascular model. The method may also include setting at least one of a first boundary condition of the first vascular model or a second boundary condition of the second vascular model and determining a flow field distribution of the coupled vascular model based on the at least one of the first boundary condition or the second boundary condition. The method may further include determining hemodynamic parameters based on the flow field distribution.

A patient-specific registration jig for registering an orthopaedic surgical instrument with a bony anatomy of a patient includes a head and an adaptor coupled to the head. The head includes a patient-specific contact surface configured to contact a portion of the patient's bony anatomy such that the head can be coupled to the patient's bony anatomy in a unique position. The adaptor includes an elongated shank having a first end coupled to the head and a second end and an adaptor end attached to the second end of the elongated shank. The adaptor end is configured to be received by a clutch of the orthopaedic surgical instrument. A method for registering the orthopaedic surgical instrument using the patient-specific registration jig is also disclosed.

A patient-specific registration jig for registering an orthopaedic surgical instrument with a bony anatomy of a patient includes a head and an adaptor coupled to the head. The head includes a patient-specific contact surface configured to contact a portion of the patient's bony anatomy such that the head can be coupled to the patient's bony anatomy in a unique position. The adaptor includes an elongated shank having a first end coupled to the head and a second end and an adaptor end attached to the second end of the elongated shank. The adaptor end is configured to be received by a clutch of the orthopaedic surgical instrument. A method for registering the orthopaedic surgical instrument using the patient-specific registration jig is also disclosed.

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.