A point within a three dimensional region represented by volume data is set as a target point. A three dimensional angular range having the target point as its apex is set, and a plurality of line of sight vectors directed toward the target point are set within the three dimensional angular range. A plurality of projected images are generated by projecting the three dimensional region onto projection planes perpendicular to the line of sight vectors for each of the line of sight vectors. The generated projected images are arranged and displayed on a screen.

Apparatus and methods are described including an endoluminal device configured to move along a portion of a lumen of a subject's body, an extraluminal imaging device, and at least one computer processor. While the endoluminal device moves along the portion of the lumen, a display displays an extraluminal image of the lumen in which a first indication of a location of the lumen is shown. The extraluminal imaging device acquires a sequence of extraluminal images of the endoluminal device moving along the portion of the lumen. The indication of the location of the lumen that is displayed is updated based upon the acquired sequence of extraluminal images, and the acquired sequence of images is displayed with the updated indication of the location of the lumen overlaid upon the images. Other applications are also described.

An image processing apparatus comprises a filtering unit for performing recursive filtering on a first signal component and a second signal component that are obtained by emitting radiation at a plurality of levels of energy toward an object, and a generation unit for generating a moving image based on the first signal component and the second signal component on which the recursive filtering is performed. A filter coefficient of the recursive filtering performed on the first signal component and a filter coefficient of the recursive filtering performed on the second signal component differ from each other.

Computer vision systems and methods for real-time localization of a needle in an ultrasound video are provided. The method includes a step of receiving at a processor a plurality of frames from the ultrasound video. The method also includes the step of identifying by the processor one of the plurality of frames as a background image and a frame adjacent to the background image as a current image. The method further includes the step of performing by the processor a bitwise complement operation on the background image to generate a complement image. Moreover, the method includes the step of performing by the processor a pointwise logical AND operation on the complement image and the current image to identify the location of the needle.

An apparatus and a method determine a virtual spatial reconstruction of a surgical tool imaged in a 2D x-ray image. A reconstruction module segments a 2D image of at least one element of the surgical tool in the 2D x-ray image and a spatial reconstruction of the at least one element is implemented after the spatial configuration of the 2D image of the at least one element is determined.

The present invention relates to compensating for brain shift in catheter trajectory planning. First brain shift information is determined from an initial brain image dataset, an initial planning dataset, a patient orientation dataset, and first burr hole dataset. The brain image dataset is updated based on the first brain shift information and a trajectory of a first catheter is updated based on the updated brain image dataset. For a subsequent catheter placement, subsequent brain shift information is determined based on the updated brain image dataset, the patient orientation dataset, and a subsequent burr hole dataset. The brain image dataset is updated again based on the subsequent brain shift information. The re-updated brain image dataset is utilized to update trajectories of the subsequent catheter as well as any preceding catheters.

A system for automated detection and type classification of central venous catheters. The system includes an electronic processor that is configured to, based on an image, generate a segmentation of a potential central venous catheter using a segmentation method and extract, from the segmentation, one or more image features associated with the potential central venous catheter. The electronic processor is also configured to, based on the one or more image features, determine, using a first classifier, whether the image includes a central venous catheters and determine, using a second classifier, a type of central venous catheter included in the image.

A weighting for a roadmap method is automatically determined. A first or a second weighting image is generated from an anatomical image and an object image. For this purpose, a prespecified first weighting value is assigned to pixels belonging to a prespecified anatomical feature or to an instrument. Other pixels are assigned increasingly small weighting values at increasing distances from the anatomical feature or from the instrument toward an edge of a respective recording region according to a prespecified monotonously decreasing function in dependence upon the location. An overall weighting image is generated by combining the first and the second weighting images with one another and/or a region of interest determined using the overall weighting image are then provided as input data for an image processing algorithm.

A vessel image, which maps a vessel structure, and a device image, which maps a device, are created. A processor, depending on the device image and the vessel image, creates an overlaying image. At least one filter algorithm is applied to the device image, creating a filtered device image. The overlaying image is created by overlaying of the vessel image with the filtered device image.

One or more devices, systems, methods, and storage mediums for optical imaging medical devices, such as, but not limited to, Optical Coherence Tomography (OCT), single mode OCT, and/or multi-modal OCT apparatuses and systems, and methods and storage mediums for use with same, for calculating lumen distance(s), including based on real-time A-line signal(s), are provided herein. Examples of applications include imaging, evaluating and diagnosing biological objects, such as, but not limited to, for Gastro-intestinal, cardio and/or ophthalmic applications, and being obtained via one or more optical instruments, such as, but not limited to, optical probes, catheters, capsules and needles (e.g., a biopsy needle). Fast A-line lumen segmentation methods, which can be applied real-time to a whole arterial pullback, and devices, systems, and storage mediums for use with same, are provided herein. Techniques provided herein also improve processing efficiency and decrease calculations while achieving measurements that are more precise.