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.

An interlobar position specifying unit specifies an interlobar position in a lung field area included in a three-dimensional image. An expansion unit expands a plane area at the interlobar position in a thickness direction to generate an expansion area including an interlobar membrane. A projection processing unit processes the expansion area by a projection method that emphasizes the interlobar membrane to generate a projection image. The display control unit displays the projection image on a display.

A surgical instrument navigation system is provided that visually simulates a virtual volumetric scene of a body cavity of a patient from a point of view of a surgical instrument residing in the cavity of the patient. The surgical instrument navigation system includes: a surgical instrument; an imaging device which is operable to capture scan data representative of an internal region of interest within a given patient; a tracking subsystem that employs electro-magnetic sensing to capture in real-time position data indicative of the position of the surgical instrument; a data processor which is operable to render a volumetric, perspective image of the internal region of interest from a point of view of the surgical instrument; and a display which is operable to display the volumetric perspective image of the patient.

A System for image processing (IPS), in particular for lung imaging. The system (IPS) comprises an interface (IN) for receiving at least a part of a 3D image volume (VL) acquired by PAT an imaging apparatus (IA1) of a lung (LG) of a subject (PAT) by exposing the subject (PAT) to a first interrogating signal. A layer definer (LD) of the system (IPS) is configured to define, in the 3D image volume, a layer object (LO) that includes a representation of a surface (S) of the lung (LG). A renderer (REN) of the system (IPS) is configured to render at least a part of the layer object (LO) in 3D at a rendering view (V.sub.p) for visualization on a display device (DD).

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

Methods and apparatuses for performing automated detection of lung slide using a computing device (e.g., an ultrasound system, etc.) are disclosed. In some embodiments, the techniques determine lung sliding using one or more neural networks. In some embodiments, the neural networks are part of a process that determines probabilities of the lung sliding at one or more M-lines. In some embodiments, the techniques display one or more probabilities of lung sliding in a B-mode ultrasound image.

A plurality of radiographic images are captured of a portion of a patient in periodic motion, such as cardiac images (heartbeat motion) or lungs (breathing motion). A first subset of the captured radiographic images are identified as having a common first capture time relative to a phase of the periodic motion. A first 3D image is reconstructed using the first subset of captured radiographic images. Additional subsets of the radiographic images are processed similarly based on their common capture time relative to the phase of the periodic motion.

A similar case retrieval apparatus includes: a lesion portion acquirer that acquires partial images including lesion portion images, an image feature extractor that extracts image features of each of the plurality of partial images; a location information acquirer that acquires location information of each of the partial images; a lateral position determiner that determines the right organ or the left organ in which each of the lesion portions exists based on the location information; a unilateral distribution identifier that determines whether or not a distribution of the lesion portions is a unilateral distribution; and a similar case retriever that retrieves case data from a case database including both case data for the unilateral distribution in the right organ and case data for the unilateral distribution in the left organ when the unilateral distribution identifier identifies that the distribution of the lesion portions is the unilateral distribution.

Systems and methods for iteratively computing an image registration or an image segmentation are driven by an optimization function that includes a similarity measure component whose effect on the iterative computations is relatively mitigated based on a monitoring of volume changes of volume elements at image locations during the iterations. A system and a related method quantify a registration error by applying a series of edge detectors to input images and combining related filter responses into a combined response. The series of filters are parameterized with a filter parameter. An extremal value of the combined response is then found and a filter parameter associated with said extremal value is then returned as output. This filter parameter relates to a registration error at a given image location.

A method and device are for automatic determination of the change of a hollow organ. The method includes providing a first medical image of the organ recorded at a first time; computing a first representation of the organ in the first image; computing a first reference-line of the organ based on the first representation and providing a second medical image of the organ recorded at a second point. The method further includes computing a second representation of the organ in the second image; computing a second reference-line of the organ based on the second representation of the organ; registering of the first and second reference-line to obtain at least one of matched representations of the organ and features derived from the matched representations of the organs; and comparing at least one of the matched representations of the organs and the features derived from the matched representations of the organ.