This application disclosures a method and system for detecting a centerline of a vessel. The method may include obtaining image data, wherein the image data may include vessel data; selecting two endpoints of the vessel based on the vessel data; transforming the image data to generate a transformed image based on at least one image transformation function; and determining a path of the centerline of the vessel connecting the first endpoint of the vessel and the second endpoint of the vessel to obtain the centerline of the vessel based on the transformed image. The two endpoints of the vessel may include a first endpoint of the vessel and a second endpoint of the vessel.

The application discloses a method and system for segmenting a lung image. The method may include obtaining a target image relating to a lung region. The target image may include a plurality of image slices. The method may also include segmenting the lung region from the target image, identifying an airway structure relating to the lung region, and identifying one or more fissures in the lung region. The method may further include determining one or more pulmonary lobes in the lung region.

Even when a missing portion occurs in a solid data set on a columnar structure, an estimator for a deflection value and an accuracy of the deflection value are correctly estimated according to an extent of the missing portion and the like. A measurement accuracy estimation unit (15) is included that: calculates a deflection of a columnar structure and an extent of a missing portion, from a solid data set on the columnar structure; calculates an accuracy assessment indicator for the deflection that is acquirable when a plurality of missing portion patterns occur on a virtual basis, based on a plurality of solid data sets in each of which the calculated extent of the missing portion is smaller than a preset threshold value, the accuracy assessment indicator being calculated for each missing portion pattern; and calculates an accuracy of the deflection calculated from the solid data set, based on the calculated accuracy assessment indicator for each missing portion pattern, and based on the calculated extent of the missing portion in the solid data set.

[Problem] To perform interpretation assistance of a back portion image in real time using a general-purpose computer for any case of scoliosis.

[Solution] An interpretation assistance apparatus includes a center line creating unit that creates a center line C in a back portion image of a subject, a measurement interval designating unit that accepts designation of an arbitrary measurement interval I along the center line C, a measurement width designating unit that accepts designation of an arbitrary measurement width W toward intersecting directions of the center line C, and a measurement point coordinate obtaining unit that obtains depth coordinates of respective measurement points that are apart from the center line on both the right and left sides by the measurement width W at every measurement interval I.

A method is for providing decision-supporting data. In an embodiment, the method includes receiving photon-counting computed tomography data relating to an examination region; determining a location of a thrombus in the examination region, based on the photon-counting computed tomography data received; generating the decision-supporting data, relating to at least one of the thrombus and a vascular wall in a region of the thrombus, based on the photon-counting computed tomography data received and the location of the thrombus determined; and providing the decision-supporting data generated.

A local extension method for segmentation of anatomical treelike structures includes receiving an initial segmentation of 3D image data including an initial treelike structure. A target point in the 3D image data is defined, and a region of interest based on the target point is extracted to create a sub-image. Highly tubular voxels are detected in the sub-image, and a spillage-constrained region growing is performed using the highly tubular voxels as seed points. Connected components are extracted from the results of the region growing. The extracted components are pruned to discard components not likely to be connected to the initial treelike structure, keeping only candidate components likely to be a valid sub-tree of the initial treelike structure. The candidate components are connected to the initial treelike structure, thereby extending the initial segmentation in the region of interest.

The present disclosure relates to methods and systems for vascular image processing. The method may include obtaining an initial vascular image, generating a vascular fragment image by performing a vascular fragmentation operation on the initial vascular image, and generating, based on the vascular fragment image, a vascular centerline image.

An ultrasonic imaging system produces more diagnostic cardiac images of the left ventricle by plotting the longitudinal medial axis of the chamber between the apex and mitral valve plane as a curved line evenly spaced between the opposite walls of the myocardium. Transverse image planes are positioned orthogonal to the curved medial axis with control points positioned in the short axis view on lines evenly spaced around and emanating from the medial axis. If the short axis view is of an oval shaped chamber the transverse image is stretched to give the heart a more rounded appearance resulting in better positioning of editing control points.

A method for quantitative flow analysis of a fluid flowing in a conduit from a sequence of consecutive image frames of such a conduit, where such image frames are timely separated by a certain time interval, the method comprising: a) selecting a start image frame and an end image frame from the sequence either automatically or upon user input; b) determining a centerline of the conduit in the start image frame; c) determining a centerline of the conduit in the end image frame; d) selecting a common start point on the centerline of the start image frame and on the centerline of the end image frame either automatically or upon user input; e) selecting an end point on the centerline of the start image frame; f) selecting an end point on the centerline of the end image frame; g) calculating centerline distance between the start point and the end point of the start image frame; h) calculating centerline distance between the start point and the end point of the end image frame; and i) calculating a local flow velocity as a function of the centerline distances of g) and h) and a time interval between the start image frame and the end image frame.
A corresponding imaging device and computer program are also disclosed.

A method for pre-operative orthopedic planning includes obtaining only a high-resolution knee-joint scan of a patient, determining hip rotation center and ankle rotation center from anthropometric data based on personal data of the patient, and determining a mechanical axis of the knee joint based on the anthropometric data. The method also includes preparing at least a two-dimensional image model of the knee joint using the knee-joint scan and the determined mechanical axis, and preparing a pre-operative surgical plan based on the image of the knee joint.