An endoscopic vessel harvesting system for surgical removal of a blood vessel to be used for coronary bypass uses endoscopic instruments for isolating and severing the vessel. An endoscopic camera in the endoscopic instruments captures images from a distal tip of the instrument within a dissected tunnel around the vessel. An image processor assembles a three-dimensional model of the tunnel from a series of images captured by the endoscopic camera. An augmented-reality display coupled to the image processor renders (e.g., visibly displays to the user in their field of view) a consolidated map representing the three-dimensional model along with a marker in association with the map indicating a current location of the distal tip.

To generate a medical image with high visibility in fluorescence observation. A medical image generation apparatus (100) according to the present application includes an acquisition unit (131), a calculation unit (132), and a generation unit (134). An acquisition unit (131) acquires a first medical image captured with fluorescence of a predetermined wavelength and a second medical image captured with fluorescence of a wavelength different from the predetermined wavelength. A calculation unit (132) calculates a degree of scattering, indicating a degree of blurring of fluorescence of a living body, included in the first medical image and the second medical image acquired by the acquisition unit (131). A generation unit (134) generates an output image on the basis of at least one of the degrees of scattering calculated by the calculation unit (132).

Provided are apparatus and method for predicting biometrics using a fundus image. The method for predicting biometrics using a fundus image includes steps of preparation of a plurality of learning fundus images, generation of a learning model for predicting corresponding biometrics using the prepared data based on at least one characteristic of the fundus reflected in the prepared plurality of learning fundus images, reception of a prediction target of fundus image, and prediction of the biometrics of the subject of the prediction target of fundus image by using the generated learning model.

Embodiments discussed herein facilitate determination of risk of recurrence of atrial fibrillation (AF) after ablation based on fractal features. One example embodiment is configured to generate a binary mask of at least a portion of a CT scan of a heart of a patient with AF; compute one or more radiomic fractal-based features from at least one of the binary mask or the portion of the CT scan; provide the one or more radiomic fractal-based features to a trained machine learning (ML) classifier; and receive a prediction from the trained ML classifier of whether or not the AF will recur after AF ablation, wherein the prediction is based at least in part on the one or more radiomic fractal-based features.

A medical image segmentation method based on a U-Net, including: sending real segmentation image and original image to a generative adversarial network for data enhancement to generate a composite image with a label; then putting the composite image into original data set to obtain an expanded data set, and sending the expanded data set to improved multi-feature fusion segmentation network for training. A Dilated Convolution Module is added between the shallow and deep feature skip connections of the segmentation network to obtain receptive fields with different sizes, which enhances the fusion of detail information and deep semantics, improves the adaptability to the size of the segmentation target, and improves the medical image segmentation accuracy. The over-fitting problem that occurs when training the segmentation network is alleviated by using the expanded data set of the generative adversarial network.

Intravascular ultrasound (IVUS) imaging devices, systems, and method are provided. In one embodiment, an IVUS imaging device includes a flexible elongate member configured to be positioned within a lumen of a patient, the flexible elongate member comprising a proximal portion and a distal portion; and an imaging assembly disposed at the distal portion of the flexible elongate member. The imaging assembly includes a first ultrasound transducer operating at a first center frequency; and a second ultrasound transducer operating at a second center frequency different from the first center frequency.

The invention relates to a computing system and a computer-implemented method for classifying images of retina of eyes of subjects. A captured image of a retina is processed to obtain a plurality of different segmented images each having different selected portions of the captured image using different selection rules. The multiple segmented images are provided to respective dedicated machine learning models to output an image classification based on the respective segmented images provided as input. An ensemble classification is determined based on the multiple classifications obtained by means of the multiple trained machine learning models.

A program and the like that make a catheter system relatively easy to use. The program including a non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process comprising: acquiring a tomographic image generated using a diagnostic imaging catheter inserted into a lumen organ; and inputting the acquired tomographic image to a first model configured to output types of a plurality of objects included in the tomographic image and ranges of the respective objects in association with each other when the tomographic image is input, and outputting the types and ranges of the objects output from the first model.

An image processing method performed by a processor and including detecting positions of plural vortex veins in a fundus image of an examined eye, and computing a center of distribution of the plural detected vortex vein positions.

Disclosed is an intraluminal imaging system, including an intraluminal imaging catheter or guidewire configured to be positioned within an anatomy of a patient, and a processor circuit in communication with the imaging catheter or guidewire, wherein the processor circuit is configured to receive a plurality of cross-sectional images of the anatomy from the imaging catheter or guidewire. The processor is further configured to compute, using image processing of at least one of the cross-sectional images, a value of the anatomy, estimate a cross-sectional shape of the anatomy to be circular, calculate a diameter of the anatomy based on the computed value and the estimated circular shape, and output the diameter of the anatomy to a display.