FPL-UDA: Filtered Pseudo Label-Based Unsupervised Cross-Modality Adaptation for Vestibular Schwannoma Segmentation

Automatic segmentation of Vestibular Schwannoma (VS) from Magnetic Resonance Imaging (MRI) will help patient management and improve clinical workflow. This paper aims to adapt a model trained with annotated ceT1 images to segment VS from hrT2 images, without annotations of the latter. The proposed method is named as Filtered Pseudo Label-based Unsupervised Domain Adaptation (FPL-UDA) and consists of three components: 1) an image translator converting hrT2 images to pseudo ceT1 images, where a two-stage translation strategy is proposed to deal with images with VS in various sizes, 2) a pseudo label generator trained with ceT1 images to provide pseudo labels for the pseudo ceT1 images, where a GAN-based data augmentation method is proposed to deal with the domain gap between them, and 3) a final segmentor trained with hrT2 images and the corresponding pseudo labels, where an uncertainty-based filtering is used to select high-quality pseudo labels to improve the segmentor’s robustness. Experimental results with a public VS dataset showed that our method achieved an average Dice of 81.52% for VS segmentation from hrT2 images, which outperformed existing unsupervised cross-modality adaptation methods.