Abstract

Myocardial segmentation is crucial for accurate assessment of cardiac functions and pathology. Automatic segmentation of myocardial tissue from magnetic resonance (MR) images is actively researched especially with cine MR data that offer high spatial and temporal resolution. Challenges arise when processing cine images acquired from different scanner systems, as segmentation networks trained using one manufacturer’s images may perform poorly on another’s due to variations between training and test data distributions. Unsupervised domain adaptation addresses the domain shift issue by transferring knowledge from a labeled source domain to an unlabeled target domain, thereby offering a general solution framework. In addition, another major challenge lies in the pixel inconsistencies among different segmented label regions, unbalancing the reliability of segmentation across various image structures. This study proposes a domain-adaptive framework for myocardial segmentation using cine MR data from various manufacturers, employing a class-imbalance self-training structure. The framework iteratively refines the segmentation model using pseudo labels generated in the target domain by adopting a class-specific label threshold that re-balances pixel inconsistencies between different label regions. Embedded in a convolutional UNet architecture tailored for segmenting cine cardiac MR images, the proposed method was tested to adapt a UNet trained with MR data from a Siemens scanner to effectively segment MR data from a Philips scanner. Results show improved segmentation quality over two other comparison methods with respect to both the Dice similarity score and the Hausdorff distance.