Method for producing result images for an examination object

Abstract

A method is for automatically producing result images for an examination object using section image data. In this case, a target structure is first of all ascertained in the section image data on the basis of a diagnostic questionnaire, and the target structure is taken as a basis for selecting an anatomical norm model whose geometry can be varied using model parameters. The norm model is automatically adapted to the target structure. The section image data are then segmented on the basis of the adapted norm model, with anatomical structures of the examination object which are relevant to the diagnostic questionnaire being separated by selecting all of the pixels within the section image data which are situated within a contour of the adapted norm model and / or at least one model part in line with the relevant structures or have a maximum discrepancy therefrom by a particular value. The relevant structures are then visually displayed separately and / or are stored for later visual display. The document also describes a corresponding image processing system.

Description

technical field [0001] The invention relates to a method for automatically generating a result image of an examination object from cross-sectional image data of the examination object concerned. Furthermore, the invention relates to an image processing system for carrying out such a method. Background technique [0002] Examination results produced by means of cross-sectional images of modality machines, such as computed tomography, magnetic resonance tomography and ultrasound systems, usually produce a multi-sectional image series of the relevant examination object. In many cases these cross-sectional images have to be processed further already during the examination or immediately after the examination in order to plan further examinations and / or to provide a diagnosis. The running of such checks is generally determined here by diagnosing the problem. In most cases, a specific organ or organ system is examined more precisely immediately after the generation of the rough ...

AI Technical Summary

Problems solved by technology

Another issue is that identifying pathology of specific internal structures in cross-sectional image data, especially in very complex anatomy, can be particularly difficult and requires some experience

thus may be prone to wrong intermediate diagnoses

This can lead to a deterioration in the quality of cross-sectional image inspection

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