Lung lobe segmentation optimization method and system based on lung segmentation

Abstract

The invention discloses a lung lobe segmentation optimization method and system based on lung segmentation, and relates to the field of medical images. The method comprises the steps that 1, a first output result of a first lung automatic segmentation algorithm model and a second output result of a second lung lobe automatic segmentation algorithm model are obtained, and the precision of the first lung automatic segmentation algorithm model is larger than that of the second lung lobe automatic segmentation algorithm model; the method also includes: step 2, carrying out point multiplication on the first output result and the second output result to obtain an independent mask of each lung lobe; step 3, respectively acquiring the maximum connected domain matrix of the independent mask of each lung lobe; step 4, performing superposition processing on each maximum connected domain matrix to obtain first optimization data; and step 5, performing calculation and elimination processing on the first optimization data to obtain a final optimization result. The invention can achieve the effect of improving the prediction precision.

Description

technical field [0001] The present invention relates to the field of medical images, in particular to a lung segmentation-based optimization method and system for lung lobe segmentation. Background technique [0002] In the field of medical images, due to the difficulty in obtaining training data, it is often difficult to train the neural network to the optimal state, which will lead to inaccurate prediction results. Due to the difficulty of delineating lung lobes, only a small number of training samples can often be obtained. Therefore, it is usually necessary to post-process the lung lobes to minimize the errors in the prediction results. Compared with the lung lobes, the lungs are much less difficult to draw because of their clearer boundaries, and it is much easier to obtain training data. At the same time, due to the clear boundary characteristics of the lungs, the training of the neural network is also easier, and the prediction results will be more accurate. [0003]...

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Problems solved by technology

This prediction method often leads to the prediction of the lung lobe mask, each lung lobe segmentation

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