ZMIS-SAM：结合小波变换的用于浮游动物显微图像实例分割的Segment Anything Model
ZMIS-SAM: Segment Anything Model Enhanced with Wavelet Transform for Zooplankton Microscopy Image Instance Segmentation
浏览论文内容
中文总结 AI 辅助
针对SAM在浮游动物显微图像实例分割中存在的问题，提出结合小波变换的ZMIS-SAM模型，通过三项核心创新实现最优性能与强泛化能力。
中文摘要 AI 辅助
浮游动物作为海洋食物链中的初级消费者，在维持海洋生态平衡方面发挥着关键作用。然而，由于Segment Anything Model（SAM）缺乏针对浮游动物的特定领域知识，其在显微图像实例分割任务中的性能有限。为解决这些挑战，我们提出了一种基于SAM和小波变换的新型实例分割模型ZMIS-SAM，可有效应对分类不准确、细长附肢分割不连续以及边界分割不完整等问题。我们的框架包含三项核心创新：ZM-ViT通过两个轻量适配器增强SAM对浮游动物形态和图像强度分布的建模能力；邻域特征聚合模块（NFAM）通过整合通用特征和领域特定特征，提升半透明细长附肢的连续分割效果；基于小波的多尺度多方向特征增强（WM2FE）模块可有效恢复高频细节，以完善边界分割的完整性。大量实验表明，ZMIS-SAM在浮游动物数据集上实现了最先进的实例分割性能，并在多个公开跨域数据集上展现出较强的泛化能力。
英文摘要
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.
发表机构
- College of Oceanography and Space Informatics, China University of Petroleum (East China)（中国石油大学（华东）海洋与空间信息学院）
机构由 AI 辅助整理，请以论文原文为准。
