There is provided a method for registration of intravital anatomical imaging modality image data and nuclear medicine image data of a patient's heart comprising: obtaining anatomical image data including a heart of a patient outputted by an anatomical intravital imaging modality; obtaining at least one nuclear medicine image data outputted by a nuclear medicine imaging modality, the nuclear medicine image data including the heart of the patient; identifying a segmentation of a network of vessels of the heart in the anatomical image data; identifying a contour of at least part of the heart in the nuclear medicine image data, the contour including at least one muscle wall border of the heart; correlating between the segmentation and the contour; registering the correlated segmentation and the correlated contour to form a registered image of the anatomical image data and the nuclear medicine image data; and providing the registered image for display.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.

A method for microscope-based super-resolution includes acquiring a to-be-processed image and at least an auxiliary image, the to-be-processed image includes a target area, the auxiliary image includes an overlapping portion with the target area, and the to-be-processed image and the auxiliary image are both microscope images of a first resolution. The method further includes registering the to-be-processed image and the auxiliary image to obtain a registered image, and extracting one or more high-resolution features from the registered image. The one or more high-resolution features represent image features of the target area in a second resolution, and the second resolution is greater than the first resolution. The method also includes reconstructing, based on the one or more high-resolution features, a target image of the second resolution corresponding to the to-be-processed image of the first resolution. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

A method for microscope-based super-resolution includes acquiring a to-be-processed image and at least an auxiliary image, the to-be-processed image includes a target area, the auxiliary image includes an overlapping portion with the target area, and the to-be-processed image and the auxiliary image are both microscope images of a first resolution. The method further includes registering the to-be-processed image and the auxiliary image to obtain a registered image, and extracting one or more high-resolution features from the registered image. The one or more high-resolution features represent image features of the target area in a second resolution, and the second resolution is greater than the first resolution. The method also includes reconstructing, based on the one or more high-resolution features, a target image of the second resolution corresponding to the to-be-processed image of the first resolution. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

An image processing method and apparatus, and a non-transitory computer-readable storage medium are provided. The method includes: acquiring an exposure duration and at least one motion component corresponding to at least one direction within the exposure duration of a current video frame; performing a first noise reduction operation on the current video frame in a two-dimensional space domain in response to a first motion component of the at least one motion component being greater than a preset motion component threshold; and performing a second noise reduction operation on the current video frame in a three-dimensional space domain in response to the at least one motion component being less than the preset motion component threshold.

An image processing method and apparatus, and a non-transitory computer-readable storage medium are provided. The method includes: acquiring an exposure duration and at least one motion component corresponding to at least one direction within the exposure duration of a current video frame; performing a first noise reduction operation on the current video frame in a two-dimensional space domain in response to a first motion component of the at least one motion component being greater than a preset motion component threshold; and performing a second noise reduction operation on the current video frame in a three-dimensional space domain in response to the at least one motion component being less than the preset motion component threshold.

Systems and methods are provided in which intraoperatively acquired surface data is employed to verify the correspondence of an intraoperatively selected spinal level with a spinal level that is pre-selected based on volumetric image data. Segmented surface data corresponding to the pre-selected spinal levels may be obtained from the volumetric image data, such that the segmented surface data corresponds to a spinal segment that is expected to be exposed and identified intraoperatively during the surgical procedure. The segmented surface data from the pre-selected spinal level, and adjacent segmented surface data from an adjacent spinal level that is adjacent to the pre-selected spinal level, is registered to the intraoperative surface data, and quality measures associated with the registration are obtained, thereby permitting an assessment or a determination of whether or not the pre-selected spinal surface (in the volumetric frame or reference) is likely to correspond to the intraoperatively selected spinal level.

Systems and methods are provided in which intraoperatively acquired surface data is employed to verify the correspondence of an intraoperatively selected spinal level with a spinal level that is pre-selected based on volumetric image data. Segmented surface data corresponding to the pre-selected spinal levels may be obtained from the volumetric image data, such that the segmented surface data corresponds to a spinal segment that is expected to be exposed and identified intraoperatively during the surgical procedure. The segmented surface data from the pre-selected spinal level, and adjacent segmented surface data from an adjacent spinal level that is adjacent to the pre-selected spinal level, is registered to the intraoperative surface data, and quality measures associated with the registration are obtained, thereby permitting an assessment or a determination of whether or not the pre-selected spinal surface (in the volumetric frame or reference) is likely to correspond to the intraoperatively selected spinal level.

A system includes a lighting module, a processing module, and photovoltaic units. Each of the photovoltaic units receives light reflected off a body portion which is illuminated by light from the lighting module, and converts light energy of the reflected light into electricity. The processing module stores modes each of which specifies a code set. When one of the modes is selected, the processing module activates the lighting module to emit light based on the code set specified by the mode thus selected. The processing module converts electrical quantities measured individually for the photovoltaic units into respective code parameters, and composes an identification code using the code parameters.