The disclosure relates generally to the field of vascular system and peripheral vascular system data collection, imaging, image processing and feature detection relating thereto. In part, the disclosure more specifically relates to methods for detecting position and size of contrast cloud in an x-ray image including with respect to a sequence of x-ray images during intravascular imaging. Methods of detecting and extracting metallic wires from x-ray images are also described herein such as guidewires used in coronary procedures. Further, methods for of registering vascular trees for one or more images, such as in sequences of x-ray images, are disclosed. In part, the disclosure relates to processing, tracking and registering angiography images and elements in such images. The registration can be performed relative to images from an intravascular imaging modality such as, for example, optical coherence tomography (OCT) or intravascular ultrasound (IVUS).

A non-transitory computer-readable medium storing computer program code executed by a computer processor that executes an imaging process comprising: acquiring a medical image generated based on a signal detected by a catheter insertable into a body lumen; estimating a cause of an image defect by inputting the acquired medical image to a model learned to output the cause of the image defect when the medical image in which the image defect occurs is input; and outputting introduction information for introducing a countermeasure for removing the estimated cause of the image defect.

A non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process of acquiring a medical image generated based on a signal detected by a catheter inserted to a lumen organ, estimating a position of an object at least included in the acquired medical image by inputting the medical image to a first learning model for estimating a position of an object included in the medical image, extracting from the medical image an image portion by using the estimated position of the object as a reference, and recognizing the object included in the extracted image portion by inputting the image portion to a second learning model for recognizing an object included in the image portion.

The disclosed image acquisition medical device and medical system make it possible to easily grasp an orientation of a distal end portion of the medical device based on an angiographic image and a tomographic image. The image acquisition medical device includes a flexible body portion that extends in an axial direction; an image sensor that is disposed in the body portion and that is configured to acquire an image of a hollow organ; and a contrast unit that protrudes toward a distal end side of the body portion and that makes an orientation of a distal end portion of the body portion visually recognizable in an angiographic image. Relative positions of the image sensor and the contrast unit in an axial rotation direction are fixed.

Aspects of the disclosure relate to systems, methods, and algorithms to train a machine learning model or neural network to classify OCT images. The neural network or machine learning model can receive annotated OCT images indicating which portions of the OCT image are blocked and which are clear as well as a classification of the OCT image as clear or blocked. After training, the neural network can be used to classify one or more new OCT images. A user interface can be provided to output the results of the classification and summarize the analysis of the one or more OCT images.

A non-transitory computer-readable medium storing a computer program executed by a computer processor to execute a process including: acquiring a medical image generated based on a signal detected by a catheter inserted into a luminal organ; detecting an object region included in the medical image by inputting the acquired medical image into a model trained for detecting the object region included in the medical image; calculating reliability of each of a plurality of boundary points located on a boundary between the detected object region and another image area in the medical image; and correcting the boundary based on a detection result of the object region by the model by performing interpolation using the boundary points at which the calculated reliability satisfies a predetermined standard.

A method performed by a computing system comprises receiving a fluoroscopic image of a patient anatomy while a portion of a medical instrument is positioned within the patient anatomy. The fluoroscopic image has a fluoroscopic frame of reference. The portion has a sensed position in an anatomic model frame of reference. The method further comprises identifying the portion in the fluoroscopic image and identifying an extracted position of the portion in the fluoroscopic frame of reference using the identified portion in the fluoroscopic image. The method further comprises registering the fluoroscopic frame of reference to the anatomic model frame of reference based on the sensed position of the portion and the extracted position of the portion.

A medical navigation system including a controller configured to: generate a three-dimensional (3D) volume based upon acquired image information of a region of interest (ROI), determine a reference path (RP) to an object-of-interest (OOI) situated within the ROI, the RP defining an on-road path (ONP) through at least one natural pathway of an organ subject to cyclical motion and an adjacent off-road path (ORP) through tissue of the organ leading to the OOI, and an exit point situated between the ONP and the ORP, query an SSD within the at least one natural pathway to obtain SSDI, determine a shape and a pose of one or more portions of the SSD in accordance with the SSDI, calculate an error between the RP and the determined shape and pose of the SSD, and/or determine when or where to exit a wall of the natural pathway and begin the ORP based upon the calculated error.

Systems and methods for image space control of a medical instrument are provided. In one example, a system is configured to display a two-dimensional medical image including a view of at least a distal end of an instrument. The system can determine, based on one or more fiducials on the instrument, a roll estimate of the instrument. The system further can receive a user input comprising a heading command to change a heading of the instrument within a plane of the medical image, or an incline command to change an incline of the instrument into or out of the plane of the medical image. Based on the roll estimate and the user input, the system can generate one or more motor commands configured to cause a robotic system coupled to the medical instrument to move the robotic medical instrument.

A marker-coordinate detecting unit detects coordinates of a stent marker on a new image when the new image is stored in an image-data storage unit; and then a correction-image creating unit creates a correction image from the new image through, for example, image transformation processing, so as to match up the detected coordinates with reference coordinates that are coordinates of the stent marker already detected by the marker-coordinate detecting unit in a first frame. An image post-processing unit then creates an image for display by performing post-processing on the correction image created by the correction-image creating unit, the post-processing including high-frequency noise reduction filtering-processing, low-frequency component removal filtering-processing, and logarithmic-image creating processing; and then a system control unit performs control of displaying a moving image of an enlarged image of a set region that is set in the image for display, together with an original image.