用小波变换增强SAM，精准分割浮游生物显微图像
ZMIS-SAM: Segment Anything Model Enhanced with Wavelet Transform for Zooplankton Microscopy Image Instance Segmentation
- 在SAM基础上引入小波变换与轻量适配器，提升形态建模能力
- 对细长附肢和边界实现连续、完整的分割，准确率显著提升
- 适合海洋生态研究者用于浮游生物自动分析
作为海洋食物链中的初级消费者，浮游生物在维持海洋生态平衡中起关键作用。然而，通用的分割任意模型（SAM）在显微图像实例分割任务中表现有限，因其缺乏浮游生物特定领域知识。为此，本文提出一种基于SAM与小波变换的新型实例分割模型ZMIS-SAM，有效解决分类不准、细长附肢断裂、边界不完整等问题。框架包含三大创新：ZM-ViT通过两个轻量级适配器增强SAM对浮游生物形态与图像强度分布的建模能力；邻域特征聚合模块（NFAM）融合通用与领域特异性特征，改善半透明细长附肢的连续分割；基于小波的多尺度多方向特征增强（WM2FE）模块有效恢复高频细节，提升边界分割完整性。大量实验表明，ZMIS-SAM在浮游生物数据集上达到当前最优性能，并在多个公开跨域数据集上展现出强泛化能力。
原文摘要 · Abstract (English)
As primary consumers in the marine food chain, zooplankton play a crucial role in maintaining marine ecological balance. However, the Segment Anything Model (SAM) exhibits limited performance in microscopic image instance segmentation due to its lack of zooplankton-specific domain knowledge. To address these challenges, we propose a novel instance segmentation model based on SAM and wavelet transform (ZMIS-SAM), effectively tackling issues such as inaccurate classification, discontinuous segmentation of slender appendages, and incomplete boundary segmentation. Our framework incorporates three core innovations: ZM-ViT enhances SAM's capability to model zooplankton morphology and image intensity distributions through two lightweight adapters, the Neighboring Feature Aggregation Module (NFAM) improves continuous segmentation of semi-transparent slender appendages by integrating general-purpose and domain-specific features, and the Wavelet-based Multi-scale Multi-directional Feature Enhancement (WM2FE) module effectively recovers high-frequency details to refine boundary segmentation completeness. Extensive experiments demonstrate that ZMIS-SAM achieves state-of-the-art instance segmentation performance on the zooplankton dataset and exhibits strong generalization capability across multiple public cross-domain datasets.
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