Automated brain lesion segmentation provides valuable information for the
analysis and intervention of patients. In particular, methods based on
convolutional neural networks (CNNs) have achieved state-of-the-art
segmentation performance. However, CNNs usually require a decent amount of
annotated data, which may be costly and time-consuming to obtain. Since
unannotated data is generally abundant, it is desirable to use unannotated data
to improve the segmentation performance for CNNs when limited annotated data is
available. In this work, we propose a semi-supervised learning (SSL) approach
to brain lesion segmentation, where unannotated data is incorporated into the
training of CNNs. We adapt the mean teacher model, which is originally
developed for SSL-based image classification, for brain lesion segmentation.
Assuming that the network should produce consistent outputs for similar inputs,
a loss of segmentation consistency is designed and integrated into a
self-ensembling framework. Specifically, we build a student model and a teacher
model, which share the same CNN architecture for segmentation. The student and
teacher models are updated alternately. At each step, the student model learns
from the teacher model by minimizing the weighted sum of the segmentation loss
computed from annotated data and the segmentation consistency loss between the
teacher and student models computed from unannotated data. Then, the teacher
model is updated by combining the updated student model with the historical
information of teacher models using an exponential moving average strategy. For
demonstration, the proposed approach was evaluated on ischemic stroke lesion
segmentation, where it improves stroke lesion segmentation with the
incorporation of unannotated data.

本文介绍了一种用于脑部损伤分割的半监督学习方法，借助未标记的数据提高了卷积神经网络的性能，并通过实验在缺乏注释的数据上获得了良好的结果。