CT image lung lobe segment segmentation method, device and system, storage medium and equipment

Abstract

The invention relates to a CT image lung lobe segment segmentation method, device and system, a storage medium and equipment. The method comprises the steps that an output lung contour in a CT image is detected, wherein the lung contour comprises an intra-lung region and an extra-lung region; in the lung contour, a machine segmentation method is used to screen out the intra-lung region, and the intra-lung region is used as a candidate region; in the 3D level of the candidate region, blood vessel segmentation and lung fissure segmentation are carried out on lung segments and lung lobes simultaneously; according to the result of blood vessel segmentation, a blood vessel tree is constructed to acquire the three-dimensional blood vessel distribution of the lungs; and the blood vessel tree andthe result of lung fissure segmentation are combined, and lung lobe segment segmentation is carried out to acquire the final segmentation result of the candidate region. According to the CT image lunglobe segment segmentation method, device and system, the storage medium and equipment, errors are effectively reduced; the diagnosis rate and accuracy rate are improved; and the limit of individual lung shape differences is eliminated.

Description

technical field [0001] The present invention relates to the field of image segmentation, in particular to a method, device, system, storage medium and equipment for lung lobe segment segmentation of CT images. Background technique [0002] Lung cancer is currently the cancer with the highest mortality rate among all cancers. Pulmonary nodules are the image manifestations of lung cancer. They appear as shadows with increased density in CT imaging. The detection and segmentation of lung nodules in lung lobes and lung segments in CT images is of great importance for early screening and evaluation of lung cancer. significance. In existing detection, 3D CT (Emmenlauer, M., etc.: "free, fast and reliable stitching of large 3d datasets." J Microscopy, 233, no.1, pp.42-60, 2009.) image data volume Large, and there are large individual differences, which brings certain difficulties to the segmentation technology of lung lobes and lung segments. [0003] Different from the applicat...

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This method ignores the differences in lung structure between different patients, and the detection results are uncertain (I.C. Sluimer, etc.: "Towards automated segmentation of the pathological lung in CT," IEEE Trans.Med.Imag., vol.24 , no.8, pp.1025–1038, Aug.2005.)

2) Establish a water-washing deformation through the analysis of the trachea and vessels. This method has a better segmentation effect for visible fissures, but poor segmentation for partially invisible segments (E.van Rikxoort, etc.: "Automatic segmentation of pulmonary lobes ro

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