Semi-supervised temporal attention network for lung 4D CT ventilation estimation

Abstract

Computed tomography (CT)-derived ventilation estimation, also known as CT ventilation imaging (CTVI), is emerging as a potentially crucial tool for designing functional avoidance radiotherapy treatment plans and evaluating therapy responses. However, most conventional CTVI methods are highly dependent on deformation fields from image registration to track volume variations, making them susceptible to registration errors and resulting in low estimation accuracy. In addition, existing deep learning-based CTVI methods typically have the issue of requiring a large amount of labeled data and cannot fully utilize temporal characteristics of 4D CT images. To address these issues, we propose a semi-supervised temporal attention (S²TA) network for lung 4D CT ventilation estimation. Specifically, the semi-supervised learning framework involves a teacher model for generating pseudo-labels from unlabeled 4D CT images, to train a student model that takes both labeled and unlabeled 4D CT images as input. The teacher model is updated as the moving average of the instantly trained student, to prevent it from being abruptly impacted by incorrect pseudo-labels. Furthermore, to fully exploit the temporal information of 4D CT images, a temporal attention architecture is designed to effectively capture the temporal relationships across multiple phases in 4D CT image sequence. Extensive experiments on three publicly available thoracic 4D CT datasets show that our proposed method can achieve higher estimation accuracy than state-of-the-art methods, which could potentially be used for lung functional avoidance radiotherapy and treatment response modeling.