刚刚，科学家发表了题为“Sequence Segmentation of Nematodes in Atlantic Cod with Multispectral Imaging Data”的文章，文章发表于期刊foods。

利用多光谱成像数据对大西洋鳕鱼线虫进行序列分割

摘要

线虫对鱼类加工业构成了重大挑战，尤其是鳕鱼。尽管技术取得了进步，但该行业仍然依赖体力劳动来检测和提取线虫。本研究介绍了自动线虫检测和区分鱼片中其他常见缺陷（如皮肤残留物和血斑）的初始步骤。VideometerLab 4 是一种先进的多光谱成像 （MSI） 系统，用于在受控条件下采集 50 片大西洋鳕鱼片的 270 张图像。总共使用 Segment Anything Model （SAM） 标记了 173 个线虫，该模型经过训练，可以仅从几个具有代表性的像素中自动分割感兴趣的对象。利用获得的数据集，我们研究了通过线虫的光谱特征识别线虫的潜力。我们结合了归一化典型判别分析 （nCDA） 来开发分割模型，这些模型经过训练可以区分鱼片内的不同成分。通过整合多个细分模型，我们旨在实现假阴性和假阳性之间的令人满意的平衡。我们的注释测试数据准确率达到 88% 、召回率达 79% 。这种方法可以通过准确识别含有线虫的鱼片来改进过程控制。使用 MSI 可最大限度地减少对状况良好的鱼片进行不必要的检查，同时提高产品安全性和质量。

关键词：多光谱成像;鱼片检查;图像处理;线虫检测 

Sequence Segmentation of Nematodes in Atlantic Cod with Multispectral Imaging Data

Abstract: Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality.

Keywords: multispectral imaging; fish fillet inspection; image processing; nematode detection