Semi-Supervised 3D Medical Image Segmentation Based on Shape Guidance and Uncertainty Estimation
In medical image segmentation tasks based on deep learning methods,a large amount of labeled data is often required.However,obtaining reliable annotations is expensive and time-consuming.To solve the above problems,a new framework is proposed for 3D medical image segmentation using a biconsistent regularized semi-supervised method with shape constraints and uncertainty estimation.Firstly,a shape constraint based on the learning target region is introduced,and the geometric constraint is strengthened by jointly learning the output of the two networks,so as to learn more reliable information.Secondly,a segmentation network is designed to generate feature maps at different scales,and multi-scale con-sistency loss is introduced to enhance its stability.However,due to the different spatial resolutions of these feature maps,forcing consistency directly on each pixel can lead to unreliable results and loss of information.Therefore,a multi-scale con-sistent learning based on uncertainty estimation is further proposed to gradually learn meaningful and reliable feature regions and enhance the robustness of the model.Experimental results show that the proposed method is superior to the popular semi-supervised medical image segmentation method due to the powerful knowledge mining ability of label-free data.
3D medical image segmentationsemi-supervised learningshape constraintsuncertainty estimates
国家科技期刊平台
NSTL
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