A medical imaging system comprises an operator terminal configured to obtain at least one image of a patient generated by a medical imaging device, receive one or more notes pertaining to the at least one image from an operator of the medical imaging device, store a clean set of images including the at least one image in at least one server, annotate the at least one image with the one or more notes to generate a set of annotated images; tag the set of annotated images as non-persistent, and store the set of annotated images in the at least one server; wherein the at least one server is configured to provide to a physician terminal both the clean set of images and the annotated set of images stored for the patient and automatically delete the one or more images tagged as non-persistent after review thereof by the physician.

An information processing apparatus (2000) detects an abnormal region (30) from a predetermined range (16) of a video frame (14). The information processing apparatus (2000) determines whether a predetermined condition is satisfied in a case where the abnormal region (30) is detected from the predetermined range (16) of a certain video frame (14) and the abnormal region (30) is not detected from the predetermined range (16) of a predetermined video frame (14) generated later than the video frame (14). In a case where the predetermined condition is not satisfied, the information processing apparatus (2000) performs a predetermined notification.

An information processing apparatus (2000) detects an abnormal region (30) from a predetermined range (16) of a video frame (14). The information processing apparatus (2000) determines whether a predetermined condition is satisfied in a case where the abnormal region (30) is detected from the predetermined range (16) of a certain video frame (14) and the abnormal region (30) is not detected from the predetermined range (16) of a predetermined video frame (14) generated later than the video frame (14). In a case where the predetermined condition is not satisfied, the information processing apparatus (2000) performs a predetermined notification.

A computer-implemented method for generating a virtual depiction of an orthodontic treatment of a patient is disclosed herein. The computer-implemented method may involve gathering a three-dimensional (3D) model modeling the patient's dentition at a specific treatment stage of an orthodontic treatment plan. An image of the patient's face and dentition may be gathered. A first set of reference points modeled on the 3D model of the patient's dentition and a second set of reference points represented on the dentition of the image of the patient may be received. The image of the patient's dentition may be projected into a 3D space to create a projected 3D model of the image of the patient's dentition. Based on a comparison of the first reference points and projections of the second set of reference points, a plurality of modified images of the patient may be constructed to depict progressive stages of a treatment plan.

In a laser cataract procedure that also corrects for astigmatism, an iris registration method compares an iris image of a patient's eye taken when the eye is not docked to a patient interface device with an iris image of the same eye that is docked to the patient interface, to calculate a rotation angle between the two images. The astigmatism axis of the eye is measured when the eye is not docked, and the measured axis is rotated by the calculated rotation angle to obtain a rotated astigmatism axis relative to the iris image of the docked eye. The laser cataract procedure is performed based on the rotated astigmatism axis. The rotation angle is calculated by optimizing a transformation that transforms the undocked iris image to match the docked iris image, where the transformation includes a dilation factor that accounts for different pupil dilation of the two iris images.

In a laser cataract procedure that also corrects for astigmatism, an iris registration method compares an iris image of a patient's eye taken when the eye is not docked to a patient interface device with an iris image of the same eye that is docked to the patient interface, to calculate a rotation angle between the two images. The astigmatism axis of the eye is measured when the eye is not docked, and the measured axis is rotated by the calculated rotation angle to obtain a rotated astigmatism axis relative to the iris image of the docked eye. The laser cataract procedure is performed based on the rotated astigmatism axis. The rotation angle is calculated by optimizing a transformation that transforms the undocked iris image to match the docked iris image, where the transformation includes a dilation factor that accounts for different pupil dilation of the two iris images.

Disclosed are a system, method, and computer program product for generating pruned tractograms of neural fiber bundles. The method includes receiving scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device. The method also includes generating an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data. The method further includes generating a density map using a set of tracts from the initial tractogram, identifying each tract that passes through a segment of the density map more than once, and setting a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value. The method further includes generating a pruned tractogram by identifying a segment having a unique tract count less than or equal to the threshold pruning value and excluding the segment from the pruned tractogram.

Disclosed are a system, method, and computer program product for generating pruned tractograms of neural fiber bundles. The method includes receiving scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device. The method also includes generating an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data. The method further includes generating a density map using a set of tracts from the initial tractogram, identifying each tract that passes through a segment of the density map more than once, and setting a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value. The method further includes generating a pruned tractogram by identifying a segment having a unique tract count less than or equal to the threshold pruning value and excluding the segment from the pruned tractogram.

The invention relates to a method for automatically planning a trajectory to be followed during a medical intervention by a medical instrument targeting an anatomy of interest of a patient, said automatic planning method comprising the steps of: acquiring at least one medical image of the anatomy of interest; determining a target point on the previously acquired image; generating a set of trajectory planning parameters from the medical image of the anatomy of interest and the previously determined target point, the set of planning parameters comprising coordinates of an entry point on the medical image. The set of parameters is generated using a machine learning method of neural network type. The invention also relates to a guiding device implementing the set of planning parameters obtained.

The invention relates to a method for automatically planning a trajectory to be followed during a medical intervention by a medical instrument targeting an anatomy of interest of a patient, said automatic planning method comprising the steps of: acquiring at least one medical image of the anatomy of interest; determining a target point on the previously acquired image; generating a set of trajectory planning parameters from the medical image of the anatomy of interest and the previously determined target point, the set of planning parameters comprising coordinates of an entry point on the medical image. The set of parameters is generated using a machine learning method of neural network type. The invention also relates to a guiding device implementing the set of planning parameters obtained.