An automatic identification method of bifurcated blood vessels based on ivoct images

Abstract

The present invention relates to a method for automatically identifying bifurcated blood vessels based on IVOCT images, the method comprising the following steps: Step 1, using an optical coherence tomography system and an angiography system to obtain IVOCT images during the pullback of the guide wire; Step 2, by The detected outline of the blood vessel determines the bifurcation of the blood vessel and determines the location of the bifurcation; step 3, displaying the bifurcation blood vessel. The embodiment of the present invention obtains bifurcation point pairs through multiple screenings, then confirms bifurcation points and removes false positives in a specific way, which improves the accuracy of judgment, can quickly and accurately detect and mark bifurcation blood vessels, and improves work efficiency .

Description

technical field [0001] The invention belongs to the field of medical image processing and the field of medical detection technology, and in particular relates to an automatic recognition method of bifurcation blood vessels based on IVOCT images. Background technique [0002] Before stent implantation, it is very important to accurately measure the lumen of the vessel, which determines the selection of stent size and the optimal position of stent placement. If the size of the stent is not appropriate, it may lead to poor stent apposition. The selection of the optimal position for stent implantation not only needs to consider the smallest lumen area, but also needs to consider bifurcation vessels. If a bifurcated vessel is covered with a stent, there is a high risk of pathological changes, such as restenosis and obstruction of blood flow to the bifurcated vessel. Therefore, it is very important to detect bifurcation vessels and measure main vessels before stent implantation. ...

AI Technical Summary

Problems solved by technology

However, the algorithm relies heavily on the detection of shadows in the binary image, and shadows depend on the threshold setting in the binary image, so the robustness of the algorithm is poor

[0005] In addition, in other existing technologies, the guide wire, catheter, and vessel contour need to be detected before bifurcation detection, and these steps need to use Dijkstra's algorithm separately, which is inefficient; and its judgment on false positives is not comprehensive , the misjudgment rate is high

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