ZMIS-SAM: Segment Anything Model Enhanced With Wavelet Transform For Zooplankton Microscopy Image Instance Segmentation

As primary consumers in the marine food chain, zooplanktonplay a crucial role in maintaining marine ecological balance. However, theSegment Anything Model (SAM) exhibits limited performance in micro-scopic image instance segmentation due to its lack of zooplankton-specificdomain knowledge. To address these challenges, we propose a novel in-stance segmentation model based on SAM and wavelet transform (ZMIS-SAM), effectively tackling issues such as inaccurate classification, dis-continuous segmentation of slender appendages, and incomplete bound-ary segmentation. Our framework incorporates three core innovations:ZM-ViT enhances SAM’s capability to model zooplankton morphologyand image intensity distributions through two lightweight adapters, theNeighboring Feature Aggregation Module (NFAM) improves continu-ous segmentation of semi-transparent slender appendages by integrat-ing general-purpose and domain-specific features, and the Wavelet-basedMulti-scale Multi-directional Feature Enhancement (WM2FE) moduleeffectively recovers high-frequency details to refine boundary segmenta-tion completeness. Extensive experiments demonstrate that ZMIS-SAMachieves state-of-the-art instance segmentation performance on the zoo-plankton dataset and exhibits strong generalization capability acrossmultiple public cross-domain datasets. Code: ZMIS-SAM.