无需标注数据，一键实现科学图像零样本分割
Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data
- 通过轻量多模态适配直接处理原始科学图像
- 在非晶与晶态催化剂样本上达94.7%准确率
- 适合无标注数据的科研人员快速部署
零样本和提示驱动模型在自然图像任务中表现优异，但在稀疏、领域特定的科学图像上常失效。我们提出Zenesis，一个无代码交互式计算机视觉平台，旨在降低科学成像工作流中的数据准备瓶颈。该平台集成轻量级多模态适配，实现对原始科学数据的零样本推理，结合人机协同优化与基于启发式的时序增强。我们在负载催化剂的聚焦离子束扫描电子显微镜（FIB-SEM）数据集上验证方法，结果表明：在非晶催化剂样本上平均准确率达0.947，交并比（IoU）为0.858，Dice分数为0.923；在晶态样本上准确率为0.987，IoU为0.857，Dice为0.923。显著优于传统方法如Otsu阈值法及独立模型SAM。Zenesis为标注数据匮乏领域提供可扩展的图像分割方案，助力科学发现。
原文摘要 · Abstract (English)
Zero-shot and prompt-based models have excelled at visual reasoning tasks by leveraging large-scale natural image corpora, but they often fail on sparse and domain-specific scientific image data. We introduce Zenesis, a no-code interactive computer vision platform designed to reduce data readiness bottlenecks in scientific imaging workflows. Zenesis integrates lightweight multimodal adaptation for zero-shot inference on raw scientific data, human-in-the-loop refinement, and heuristic-based temporal enhancement. We validate our approach on Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) datasets of catalyst-loaded membranes. Zenesis outperforms baselines, achieving an average accuracy of 0.947, Intersection over Union (IoU) of 0.858, and Dice score of 0.923 on amorphous catalyst samples; and 0.987 accuracy, 0.857 IoU, and 0.923 Dice on crystalline samples. These results represent a significant performance gain over conventional methods such as Otsu thresholding and standalone models like the Segment Anything Model (SAM). Zenesis enables effective image segmentation in domains where annotated datasets are limited, offering a scalable solution for scientific discovery.
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