Fig. 1. Framework overview.

❶: We extract per-slice 2D features for three views ($\text{z}$, $\text{y}$, and $\text{x}$) from CryoET tomogram $\mathbf{I}$ and concatenate them as $\mathbf{F}$. ❷: After segmenting the particle(s) prompted by $\mathbf{P}$ with instance segmentation mask(s), ❸: we average pool the masked features to get query feature ${\mathbf{F}}_Q$. ❹: To efficiently propose prompts for further segmentation, we match ${\mathbf{F}}_Q$ with $\mathbf{F}$ using Hierarchical Feature Matching. ❺: Finally, we adopt prompt-based 3D segmentation for semantic segmentation results $\mathbf{M}$.