Semi-supervised deep neural networks for left atrial segmentation from cardiac MRI

Abstract

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Left atrium size and function is an important but less studied prognostic marker for many cardiovascular diseases [1]. Cardiac Magnetic Resonance Imaging (MRI) can capture atrial structure with a high temporal and spatial resolution. However, diagnoses and accurate measurement of volumes from MRI often rely on time-consuming manual processing, or needs correction of software segmentation error by clinicians. Robust, automated methods are highly desirable to assist morphological analysis of dynamic changes in the heart. Ultimately this facilitates the wider application of cardiac MRI in early identification and prevention of potential cardiovascular disease. Purpose To introduce a semi-supervised teacher-student deep neural networks (requiring less labelled data) for accurate segmentation of left atrium from cardiac MRI images, and compare their performance with conventional supervised learning. Methods The basic structure for a mean teacher deep neural network is shown in Fig.1. Network training starts in a supervised manner. At each MRI batch input, both the student and the teacher model evaluate the images with applied noise. The student network is updated by gradient descent and segmentation loss is obtained by comparing its prediction with the label. The exponential moving average (EMA) of the student weight is then migrated to update the teacher network. Consistency loss between the student’s and teacher’s prediction (filtered by uncertainty map in Uncertainty-Aware Mean Teacher, UA-MT [2]) is obtained to jointly update the student. Unlabeled images continually improve the network without using supervised loss. We compared three semi-supervised teacher-student networks UA-MT, Hierarchical Consistency Regularized Mean Teacher (HCR-MT) and Semi-supervised Contrastive Consistency model (SCC), with the supervised network TransUNnet [3], using benchmark cardiac MRI dataset from MICCAI 2018 Atrial Segmentation Challenge [4]. Out of 20 MRI scans (1760 images), 16 scans were used for training, and 4 scans for testing. For semi-supervised learning, 75% of the training set was used as labelled data and 25% was assumed to be unlabeled. In the supervised learning, all labels were enabled. Results Example segmentation results from three semi-supervised models are shown in Fig 2. For comparison, the fully supervised TransUNnet produced an accuracy of 76%. Benefiting from effective teacher-student learning, the semi-supervised models achieved a superior accuracy of 89%, while also requiring less labelled data. Conclusion The proposed teacher-student deep neural network facilitates effective semi-supervised learning, and outperforms supervised TransUNet in segmenting the left atrium in cardiac MRI. Our semi-supervised approach uses less training data, reducing tedious and time-consuming manual labelling tasks, ultimately unlocking the ability to effectively utilise the full power of deep learning in medical applications.