Fig. 2.

Our proposed framework consists of three main parts: 1) TANQ-based image translation from ${\text{ceT}}_1$ to ${\text{hrT}}_2$ images, 2) Multi-view pseudo-${\text{hrT}}_2$ representation via CycleGAN, 3) Construction of a VS/cochlea segmentation model using multi-view pseudo-${\text{hrT}}_2$ images and self-training with real-${\text{hrT}}_2$ images. Specifically, TANQ divides the features based on the ${\text{ceT}}_1$ labels in both the encoder and decoder, applying target-aware normalization. Furthermore, it includes an additional decoder called SegDecoder. The Encoder E extracts features from both the real ${\text{ceT}}_1$ images and pseudo-${\text{hrT}}_2$ images and then calculates the contrastive loss between selected features using a sorted attention matrix.