Toshiba Medical Systems Corporation

Kabushiki Kaisha Toshiba

Canon Medical Systems Corporation

Toshiba Medical Visualization Systems Europe, Limited

Akinola Akinyemi

Ian Poole

Marco Razeto

Costas Plakas

Jim Piper

Sean Daniel Murphy

Sebastian Meller

Chris McGough

Jill Barnaby

Tyler Stroud

Muhammad Haris Bhatti

An imaging data processing apparatus comprises a registration unit configured to obtain a hierarchical registration between a reference data set and volumetric medical imaging data, the reference data set comprising a plurality of anatomical structures, wherein the hierarchical registration comprises an initial registration and at least one further registration, and the at least one further registration is obtained by the registration unit by modifying the initial registration, and a segmentation unit for segmenting at least part of the volumetric medical imaging data in dependence on the hierarchical registration. The initial registration comprises a registration between at least part of the reference data set and at least part of the volumetric medical imaging data, the at least part of the reference data set comprising a first anatomical structure or first group of anatomical structures. The at least one further registration comprises a registration between a subset of the reference data set and at least part of the volumetric medical imaging data, the subset of the reference data set comprising a second, different anatomical structure or second, different group of anatomical structures.

Method of, and apparatus for, registration and segmentation of medical imaging data

Apparatus for performing an imaging procedure comprising processing volumetric image data comprising an image processing unit configured to obtain first image data representative of a region including at least one vessel and at least one associated feature, and second image data representative of at least part of the region, an image refinement unit configured to process the first image data and the second image data to produce a combined image representative of the at least one vessel, wherein the associated feature is removed or reduced in intensity in the combined image, and a rendering unit configured to render the combined image as a simulated view that simulates a view obtainable from an alternative imaging procedure.

Apparatus for, and method of, processing volumetric medical image data

According to one embodiment there is provided a method of selecting a plurality of M atlases from among a larger group of N candidate atlases to form a multi-atlas data set to be used for computer automated segmentation of novel image data sets to mark objects of interest therein. A set of candidate atlases is used containing a reference image data set and segmentation data. Each of the candidate atlases is segmented against the others in a leave-one-out strategy, in which the candidate atlases are used as training data for each other. For each candidate atlas in turn, the following is carried out: registering; segmenting; computing an overlap; computing a value of the similarity measure for each of the registrations; and obtaining a set of regression parameters by performing a regression with the similarity measure being the independent variable and the overlap being the dependent variable.

Image segmentation

According to one embodiment there is provided a method of selecting a plurality of M atlases from among a larger group of N candidate atlases to form a multi-atlas data set to be used for computer automated segmentation of novel image data sets to mark objects of interest therein. A set of candidate atlases is used containing a reference image data set and segmentation data. Each of the candidate atlases is segmented against the others in a leave-one-out strategy, in which the candidate atlases are used as training data for each other. For each candidate atlas in turn, the following is carried out: registering; segmenting; computing an overlap; computing a value of the similarity measure for each of the registrations; and obtaining a set of regression parameters by performing a regression with the similarity measure being the independent variable and the overlap being the dependent variable. The M atlases are then selected from among all the N candidate atlases to form the multi-atlas data set, the M atlases being those atlases determined to collectively provide the highest aggregate overlap over all the training data image sets.

A polyline tree representation of a coronary artery tree imaged in a volume data set is obtained, and its topology is extracted to give a topological representation indicating the relative positions of vessels in the tree. The topological representation is compared with a set of topological rules to find possible anatomical classifications for each vessel, and a set of candidate labeled polyline trees is generated by labeling the polyline tree with labels showing each combination of possible anatomical classifications. Each candidate labeled tree is filtered according to a set of geometric rules pertaining to spatial characteristics of vessels in arterial trees, and any labeled tree not satisfying the geometric rules is rejected A figure of merit is calculated for each remaining candidate by comparing features of the vessels measured from the polyline tree and from the volume data set with features of correctly classified vessels in other data sets to determine the probable correctness of the labeling of each candidate, and the candidate with the best figure of merit is selected as showing the proper classification of the vessels.

Growing community of inventors

Edinburgh, United Kingdom

Akinola Akinyemi