Diagnostic tool for eye disease detection using a smartphone. At least some of the example embodiments are methods including capturing, by way of a camera lens on a device, an image of an eye to create a raw specimen; processing the raw specimen to create a processes specimen; performing edge detection on the processed specimen to detect a boundary of a cornea; extracting a region of interest of the cornea; identifying a boundary of the region of interest using a boundary tracing technique to identify a second boundary; analyzing the second boundary of the region of interest, by measuring a slope of the second boundary; and classifying the region of interest as including an eye disease, based on the analyzing the second boundary.

A virtual satellite system may receive, re-project to a spatial resolution and interpolate to a desired temporal resolution, georeferenced data representing an image of a geographic region from a plurality of different satellites. Bias in the georeferenced data between the plurality of satellites is determined and based on which satellite's image data contains an identified minimum spatial resolution, vegetation index data may be set to one of the satellite's data, which may or may not be adjusted. A target image may be generated based on the set vegetation index data.

An insole design method and an insole design system are provided, and the method includes: capturing an uncompressed free foot model by a depth camera and obtaining a free foot model three-dimensional image; capturing a pressed foot model stepped on a transparent pedal by the depth camera and obtaining a pressed foot model three-dimensional image; aligning the free foot model three-dimensional image with the pressed foot model three-dimensional image; calculating and obtaining a plantar deformation quantity according to the aligned free foot model three-dimensional image and the aligned pressed foot model three-dimensional image; and completing the designed insole according to a sole projection plane or a three-dimensional profile of the specific sole and the plantar deformation quantity.

The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

This image processing apparatus is provided with an image acquisition unit for generating a concentration change image and a control unit for performing control for displaying a blood vessel image and a concentration change image, and the control unit is configured to perform control for accepting a selection of a target region on the blood vessel image displayed on the display unit and for displaying the concentration change image corresponding to the selected target region.

A method include obtaining at least one first PET image of a subject acquired by a PET scanner and at least one first MR image of the subject acquired by an MR scanner. The method may also include obtaining a target neural network model. The target neural network model may provide a mapping relationship between PET images, MR images, and corresponding attenuation correction data, and output attenuation correction data associated with a specific PET image of the PET images. The method may further include generating first attenuation correction data corresponding to the subject using the target neural network model based on the at least one first PET image and the at least one first MR image of the subject, and determining a target PET image of the subject based on the first attenuation correction data corresponding to the subject.

Embodiments of the disclosure provide methods, apparatuses, and computer-readable media for generating image reports. In one embodiment, the method includes: obtaining an image to be analyzed; determining at least one reference image corresponding to the image to be analyzed, the at least one reference image corresponding to a respective reference image report; and generating, based on the reference image report, an image report corresponding to the image to be analyzed. In the method for generating an image report provided by the embodiments, by obtaining an image to be analyzed is obtained, at least one reference image corresponding to the image to be analyzed is determined; and an image report corresponding to the image to be analyzed is generated based on a respective reference image report corresponding to the at least one reference image. Therefore, medical staff are assisted in quickly writing an image report, thereby ensuring the quality and the efficiency of the image report, reducing labor costs and time costs required for collating the image report, and further improving the practicability of the method.

The present disclosure is directed to a method and device for automatically predicting FFR based on images of vessel. The method for automatically predicting FFR based on images of a vessel. The method comprises a step of receiving the images of a vessel acquired by an imaging device. Then, a sequence of flow speeds at a sequence of positions on a centerline of the vessel is acquired by a processor. A sequence of first features at the sequence of positions on a centerline of the vessel are acquired by the processor, by fusing structure-related features and flow speeds and using a convolutional neural network. Then, a sequence of FFR at the sequence of positions is determined by the processor through using a sequence-to-sequence neural network on the basis of the sequence of first features.

The subject matter discussed herein relates to the automatic, real-time registration of pre-operative magnetic resonance imaging (MRI) data to intra-operative ultrasound (US) data (e.g., reconstructed images or unreconstructed data), such as to facilitate surgical guidance or other interventional procedures. In one such example, brain structures (or other suitable anatomic features or structures) are automatically segmented in pre-operative and intra-operative ultrasound data. Thereafter, anatomic structure (e.g., brain structure) guided registration is applied between pre-operative and intra-operative ultrasound data to account for non-linear deformation of the imaged anatomic structure. MR images that are pre-registered to pre-operative ultrasound images are then given the same nonlinear spatial transformation to align the MR images with intra-operative ultrasound images to provide surgical guidance.

A method for providing a prognosis data record includes receiving a first image data record relating to an examination region of an examination object, and receiving an operating parameter of a medical object that is arranged at the examination region of the examination object and positioning information of the medical object that is arranged at the examination region. The prognosis data record is created by applying a trained function to input data. The input data is based on the first image data record, the at least one operating parameter, and the positioning information of the medical object. At least one parameter of the trained function is based on a comparison with a first comparison image data record. As compared with the first image data record, the first comparison image data record includes changes influenced by the medical object at the examination region. The prognosis data record is provided.