Automatic segmentation model combining U-Net and level set method for medical images

摘要

We introduce a new method that combines a constrained term and level set method for the automated segmentation of medical image. There are two types of constrained terms, fully automatic and semiautomatic. It is fully automatic to use the U-Net's segmentation result as a constrained term, and the manual segmentation result as a constrained term is semi-automatic. The level set method does not require a large training set and is theoretically very explanatory, but is usually sensitive to the initial contour. The U-Net can segment more complex medical images, but requires a large number of manually labeled images and usually needs to be normalized to produce a good generalization. Therefore, the combination of these methods combines the advantages of both methods, resulting in a method that requires a small training set and produces accurate segmentation results. We test our method on the melanoma and left ventricle images. Among them, when segmenting melanoma images, our semi-automatic segmentation and full-automatic segmentation results are better than the U-Net and RSF segmentation results alone. When segmenting the left ventricle images, our semi-automatic segmentation result is better than the RSF segmentation result. (C) 2020 Elsevier Ltd. All rights reserved.