In an image adjustment system, an image display device displays a captured image that is adjusted by an image adjustment device. The image adjustment device includes an image processor and an image generator. The image generator generates a spherical surface image. The image processor acquires the spherical surface image from the image generator to display the spherical surface image on the image display device on the basis of instruction information output from a controller. The image generator adjusts the captured image in accordance with a rotation of the spherical surface image, corrects camera shake in the captured image adjusted, and determines that a travel direction of a camera is changed when the captured image that is camera-shake corrected is changed by a predetermined angle or greater.

In an image adjustment system, an image display device displays a captured image that is adjusted by an image adjustment device. The image adjustment device includes an image processor and an image generator. The image generator generates a spherical surface image. The image processor acquires the spherical surface image from the image generator to display the spherical surface image on the image display device on the basis of instruction information output from a controller. The image generator adjusts the captured image in accordance with a rotation of the spherical surface image, corrects camera shake in the captured image adjusted, and determines that a travel direction of a camera is changed when the captured image that is camera-shake corrected is changed by a predetermined angle or greater.

Disclosed is an automatic reorientation method from an SPECT three-dimensional reconstructed image to a standard view, wherein a rigid registration parameter P between a SPECT three-dimensional reconstructed image A and a standard SPECT image R is extracted by using a rigid registration algorithm to form a mapping database of A and P; features of the image A are extracted by using a three-layer convolution module, and are converted into a 6-dimensional feature vector T after three times of full connection, and T is applied to A through a spatial transformer network to form an orientation result predicted by the network, thus establishing the automatic reorientation model of the SPECT three-dimensional reconstructed image. The SPECT three-dimensional reconstructed image to be orientated is taken as an input. A standard view can be obtained by using the automatic reorientation model of the SPECT three-dimensional reconstructed image for automatic turning.

Disclosed is an automatic reorientation method from an SPECT three-dimensional reconstructed image to a standard view, wherein a rigid registration parameter P between a SPECT three-dimensional reconstructed image A and a standard SPECT image R is extracted by using a rigid registration algorithm to form a mapping database of A and P; features of the image A are extracted by using a three-layer convolution module, and are converted into a 6-dimensional feature vector T after three times of full connection, and T is applied to A through a spatial transformer network to form an orientation result predicted by the network, thus establishing the automatic reorientation model of the SPECT three-dimensional reconstructed image. The SPECT three-dimensional reconstructed image to be orientated is taken as an input. A standard view can be obtained by using the automatic reorientation model of the SPECT three-dimensional reconstructed image for automatic turning.

A method comprising: obtaining an image; identifying a rotation angle for the image by processing the image with a first neural network; rotating the image by the identified rotation angle to generate a rotated image; classifying the image with a second neural network; and outputting an indication of an outcome of the classification, wherein the first neural network is trained, at least in part, based on a categorical distance between training data and an output that is produced by the first neural network.

A method comprising: obtaining an image; identifying a rotation angle for the image by processing the image with a first neural network; rotating the image by the identified rotation angle to generate a rotated image; classifying the image with a second neural network; and outputting an indication of an outcome of the classification, wherein the first neural network is trained, at least in part, based on a categorical distance between training data and an output that is produced by the first neural network.

A method which may effectively provide an endoscope or other surgical instrument with a synthetic multi-camera array may comprise capturing using one or more cameras located at a distal tip of the surgical instrument, a set of images comprising first and second images. For each image in such set of images, that image may be captured by a corresponding camera from the one or more cameras, may be captured when the distal dip of the instrument is located at a corresponding point in space. Such a method may also comprise generating a three dimensional image based on compositing representations of a structure in the first and second image after applying a non-rigid transformation to one or more of those representations.

A method which may effectively provide an endoscope or other surgical instrument with a synthetic multi-camera array may comprise capturing using one or more cameras located at a distal tip of the surgical instrument, a set of images comprising first and second images. For each image in such set of images, that image may be captured by a corresponding camera from the one or more cameras, may be captured when the distal dip of the instrument is located at a corresponding point in space. Such a method may also comprise generating a three dimensional image based on compositing representations of a structure in the first and second image after applying a non-rigid transformation to one or more of those representations.

In some embodiments, the system is directed to medical assessment software for analyzing one or more medical conditions and enabling communication between a medical professional and a patient. In some embodiments, the system includes one or more graphical user interfaces configured to enable a medical professional to execute one or more of scheduling a virtual appointment, view a virtual schedule, check patients in/out, enter new patients into the system, request patient recorded outcomes, and view patient progress. In some embodiments, the system is configured to implement an artificial intelligence (AI) algorithm configured to identify one or more unique features within the one or more images and use the one or more unique features as one or more fiducials during an analysis of the one or more images. In some embodiments, the analysis includes a determination of whether an abnormal condition associated with an area of skin is progressing toward healing.

In some embodiments, the system is directed to medical assessment software for analyzing one or more medical conditions and enabling communication between a medical professional and a patient. In some embodiments, the system includes one or more graphical user interfaces configured to enable a medical professional to execute one or more of scheduling a virtual appointment, view a virtual schedule, check patients in/out, enter new patients into the system, request patient recorded outcomes, and view patient progress. In some embodiments, the system is configured to implement an artificial intelligence (AI) algorithm configured to identify one or more unique features within the one or more images and use the one or more unique features as one or more fiducials during an analysis of the one or more images. In some embodiments, the analysis includes a determination of whether an abnormal condition associated with an area of skin is progressing toward healing.