Model architecture of the proposed Astro-UNETR model. The 3D data cubes are input to the Astro-UNETR, which employs swin-transformer blocks Z. Liu et al. (2021) to learn the 3D semantic representation of superbubble morphology. High-level features are refined by a bottleneck layer and then upsampled to their original dimensions via deconvolution layers and ResNet blocks, producing a 3D semantic segmentation of all bubbles.