Medical image labeling system

Abstract

The invention relates to a medical image labeling system. The medical image annotation system comprises an image acquisition module, a display module and an annotation module. A three-dimensional medical image is obtained through the image acquisition module, and then two-dimensional images of a coronal plane, a sagittal plane and a transverse plane of a three-dimensional medical image can be respectively displayed in different areas of a display interface by utilizing the display module; the annotation module can be used for obtaining the position surface annotation position of a user on thetwo-dimensional image of any position surface in the display interface, and the position surface annotation position corresponds to the three-dimensional medical image to obtain the three-dimensionalannotation position; the display module can be used for displaying the position information of the three-dimensional annotation position corresponding to each position surface on each position surfacein the display interface. By means of the system, the annotation of the specific position in the three-dimensional medical image is achieved, so convenience is achieved and high annotation efficiencyis achieved; in addition, corresponding voxels can be obtained based on the image data of the bit plane annotation position, and detailed image information of the three-dimensional annotation position can be obtained conveniently.

Description

technical field

[0001] The invention relates to the field of medical technology, in particular to a medical image labeling system. Background technique

[0002] In medicine, the symptoms of diseases in the human body basically exist in three dimensions. However, images obtained after general radiological examination are displayed as two-dimensional images, and each image in a set of two-dimensional images usually belongs to a physiological section at a different depth. With the development of artificial intelligence technology, in the field of medical diagnosis, the use of trained neural network models to analyze and evaluate medical images has become an important auxiliary diagnostic method. The training of the neural network model requires a large number of manually labeled medical images as training data, and the medical images need to be labeled by experienced doctors to ensure the correctness of the labeled information. The existing medical image labeling method usual...

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