Abstract

Info

Title:: Semi-supervised learning for brain tumor segmentation through 3DSEU-Net as uncertainty-aware mean teacher and cyclical focal loss

Author(s):: DUAN Yifan; XIAO Hongbing; Rahman Md Mostafizur; School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

Keywords:: Keywords: three-dimensional convolutional neural network squeeze and excitation semi-supervised learning; brain tumor segmentation cyclical focal loss

Abstract:: Abstract: The accurate localization and segmentation of brain tumors greatly affects the survival rate of glioma patients and the determination of treatment schemes. Generating accurate annotations in three-dimensional (3D) magnetic resonance imaging (MRI) requires a lot of professional?nowledge and is time-consuming. The semi-supervised learning using a small amount of labeled data and a large amount of unlabeled data is more practical in clinic. Herein a 3DSEU-Net in which squeeze and excitation block is introduced and combined with skip connections is proposed as teacher and student networks in the semi-supervised model, so that the richer and more robust structural and detailed features can be extracted from 3D medical image. During training, the teacher model guides the student model by quantifying uncertainties, which makes the student model learn the results with higher degree of confidence. The proposed model is able to learn more knowledge under the condition that only a small amount of labeled data is available, thereby improving the segmentation accuracy of brain tumors. In the case of only 25 labeled data, the proposed method improves segmentation accuracy by 12.9% over fully supervised learning, and has a highest segmentation accuracy of 81.41%, outperforming 6 semi-supervised methods currently reproducible on the same benchmark. These results verify the feasibility and effectiveness of the proposed method.