Augmented Multiple Perturbation Dual Mean Teacher Model for Semi-Supervised Intracranial Haemorrhage Segmentation

Abstract

Generally, there are two problems restrict the intracranial haemorrhage (ICH) segmentation task: scarcity of labelled data, and poor accuracy of ICH segmentation. To address these two issues, we propose a semi-supervised ICH segmentation model and a dedicated ICH segmentation backbone network. Our approach aims at leveraging semi-supervised modelling so as to alleviate the challenge of limited labelled data availability, while the dedicated ICH segmentation backbone network further enhances the segmentation precision. An augmented multiple perturbation dual mean teacher model is designed. Based on it, the prediction accuracy may be improved by a more stringent confidence-weighted cross-entropy (CW-CE) loss, and the feature perturbation may be increased using adversarial feature perturbation for the purpose of improving the generalization ability and efficiency of consistent learning. In the ICH segmentation backbone network, we promote the segmentation accuracy by extracting both local and global features of ICH and fusing them in depth. We also fuse the features with rich details from the upper encoder during the up-sampling process to reduce the loss of feature information. Experiments on our private dataset ICHDS, and the public dataset IN22SD demonstrate that our model outperforms current state-of-the-art ICH segmentation models, achieving a maximum improvement of over 10% in Dice and exhibiting the best overall performance.