CT image interaction segmentation method and device

Abstract

The present invention provides a CT image interaction segmentation method and device. The method comprises the steps of: regulating a CT intensity threshold range, obtaining portions to be displayed having CT intensity values belonging to the intensity threshold range, and performing marking and coloring of the portions to be displayed, employing the volume rending technology to simulate contour surface display, and displaying an original data volume rending view; determining areas to be segmented in the original data volume rending view through a marking tool, employing a filling tool to calculate the areas to be segmented, generating an initial segmentation result, and employing a corrosion expansion tool to regulate the initial segmentation result; and on the basis of the initial segmentation result, employing a marking tool to select areas without segmentation in the areas to be segmented, generating a segmentation result, and displaying a segmentation volume rending view. The CT image interaction segmentation method and device can employ a visual interaction means to simplify the segmentation process so as to save a lot of segmentation time and facilitate users' usage.

Description

technical field [0001] The present invention relates to the field of digital medical treatment, in particular to a CT (Computed Tomography, computerized tomography) image interactive segmentation method and device. Background technique [0002] In the field of CT image segmentation, the currently popular software in the world includes Seg3D from the University of Utah in the United States and the MITK workbench from the German Cancer Research Center. These software provide tools such as filtering, thresholding, etc. to generate a preliminary segmentation of the object of interest. Next, it is necessary to manually smear and erase marks on each slice, and check other views to confirm the segmentation effect, so as to obtain the final segmentation. When actually segmenting data, a lot of manual interaction is still required, for example, it is necessary to use the brush tool to smear image slices one by one, and the number of slice layers in a CT image is often as many as hun...

AI Technical Summary

Problems solved by technology

When actually segmenting data, a lot of manual interaction is still required, for example, it is necessary to use the brush tool to smear image slices one by one, and the number of slice layers in a CT image is often as many as hundreds of layers. Using this method to complete data segmentation is very time-consuming and complete. Segmentation of the target object in the entire CT usually takes 1 to 2 hours, and it is difficult to segment small structures such as blood vessels

In recent years, research work has proposed several automatic segmentation methods according to different segmentation objects, but these methods have narrow applicability and low precision, and are difficult to be directly applied in actual operation.

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