Semi-supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model

Abstract

Automated brain lesion segmentation provides valuable information for the analysis and intervention of patients. In particular, methods that are 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. Self-ensembling exploits the information in the intermediate training steps, and the ensemble prediction based on the information can be closer to the correct result than the single latest model. To exploit such information, 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. Results indicate that the proposed method improves stroke lesion segmentation with the incorporation of unannotated data and outperforms competing SSL-based methods.

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images

“…For example, Nie et al [43] proposed an attention-based semi-supervised deep network for pelvic organ segmentation, in which a semi-supervised region-attention loss is developed to address the insufficient data issue for training deep learning models. Cui et al [44] modified a mean teacher framework for the task of stroke lesion segmentation in MR images. Zhao et al [45] proposed a semi-supervised segmentation method based on a self-ensemble architecture and a random patch-size training strategy.…”

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

“…For example, Nie et al [43] proposed an attention-based semisupervised deep network for pelvic organ segmentation, in which a semi-supervised region-attention loss is developed to address the insufficient data issue for training deep learning models. Cui et al [44] modified a mean teacher framework for the task of stroke lesion segmentation in MR images. Zhao et al [45] proposed a semi-supervised segmentation method based on a self-ensemble architecture and a random patchsize training strategy.…”

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

“…(2020a) . Cui et al. (2019) proposed an adapted mean teacher model to improve accuracy of brain lesion segmentation leveraging both annotated and unannotated data.…”