Supervoxel sequence lung image 3D pulmonary nodule segmentation method based on multimodal data

Abstract

The invention discloses a supervoxel sequence lung image 3D pulmonary nodule segmentation method based on multimodal data. The method comprises the following steps: A) extracting sequence lung parenchyma images through superpixel segmentation and self-generating neuronal forest clustering; B) registering the sequence lung parenchyma images through PET / CT multimodal data based on mutual information; C) marking and extracting an accurate sequence pulmonary nodule region through a multi-scale variable circular template matching algorithm; and D) carrying out three-dimensional reconstruction on the sequence pulmonary nodule images through a supervoxel 3D region growth algorithm to obtain a final three-dimensional shape of pulmonary nodules. The method forms a 3D reconstruction area of the pulmonary nodules through the supervoxel 3D region growth algorithm, and can reflect dynamic relation between pulmonary lesions and surrounding tissues, so that features of shape, size and appearance of the pulmonary nodules as well as adhesion conditions of the pulmonary nodules with surrounding pleura or blood vessels can be known visually.

Description

technical field [0001] The invention belongs to the field of medical image processing, and relates to segmentation and three-dimensional reconstruction of pulmonary nodules, in particular to a method for supervoxel three-dimensional segmentation and reconstruction of pulmonary nodules based on multimodal data of medical PET and CT images. Background technique [0002] Three-dimensional reconstruction can reflect the dynamic relationship between lung lesions and surrounding tissues, and it is convenient to intuitively understand the shape, size, appearance and other forms of pulmonary nodules and the pulling situation with the surrounding pleura or blood vessels, which is helpful for doctors to intuitively understand the condition It can also guide the operation of complex anatomical areas to improve the quality of surgery and reduce the risk of surgery, which is worthy of further clinical application. The current clinical application of medical imaging mostly produces two-di...

AI Technical Summary

Problems solved by technology

At present, with the maturity of 3D reconstruction technology, its application in clinical diagnosis and treatment is becoming more and more extensive, but the segmentation technology based on 3D images of lung lesions has not yet reached a relatively mature solution.

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