At present,segmentation for medical image is mainly based on fully supervised model training,which consumes a lot of time and labor for dataset labeling.To address this issue,we propose a semi-supervised medical image...At present,segmentation for medical image is mainly based on fully supervised model training,which consumes a lot of time and labor for dataset labeling.To address this issue,we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries.The network is mainly composed of two parts:a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results.In the initial stage of network training,a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination capabilities.Then a semi-supervised method is adopted to train the model,in which the discriminant network will generate pseudo-labels on the results of the segmentation for semi-supervised training of the segmentation network.The proposed method can use a small part of annotated dataset to realize the segmentation of medical images and effectively solve the problem of insufficient medical image annotation data.展开更多
基金supported in part by the National Natural Science Foundation of China (No.62002392)in part by the Key Research and Development Plan of Hunan Province (No.2019SK2022)+1 种基金in part by the Natural Science Foundation of Hunan Province (No.2020JJ4140 and 2020JJ4141)in part by the Postgraduate Excellent teaching team Project of Hunan Province[Grant[2019]370–133]。
文摘At present,segmentation for medical image is mainly based on fully supervised model training,which consumes a lot of time and labor for dataset labeling.To address this issue,we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries.The network is mainly composed of two parts:a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results.In the initial stage of network training,a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination capabilities.Then a semi-supervised method is adopted to train the model,in which the discriminant network will generate pseudo-labels on the results of the segmentation for semi-supervised training of the segmentation network.The proposed method can use a small part of annotated dataset to realize the segmentation of medical images and effectively solve the problem of insufficient medical image annotation data.
