论文标题
部分可观测时空混沌系统的无模型预测
A CNN Framenwork Based on Line Annotations for Detecting Nematodes in Microscopic Images
论文作者
论文摘要
植物寄生线虫在全球范围内造成农作物植物的损害。对图像数据的可靠检测是监测此类线虫的先决条件，以及许多涉及线虫秀丽隐杆线虫（一种常见模型生物）的生物学研究。在这里，我们提出了一个基于卷积神经网络（CNN）的微观图像中检测蠕虫形对象的框架。我们注释沿着身体弯曲线的线虫，它比边界盒更适合蠕虫物体。训练有素的模型可以预测蠕虫骨架和身体终点。终点可以解开骨骼，从中，通过估算沿骨骼的每个位置的身体宽度来重建分割面罩。有了轻量重量的骨干网络，我们在马铃薯囊肿线虫数据集上获得了75.85％的精度，73.02％的召回率和84.20％的精度，在公共秀丽隐杆线虫数据集中召回了85.63％的召回。
Plant parasitic nematodes cause damage to crop plants on a global scale. Robust detection on image data is a prerequisite for monitoring such nematodes, as well as for many biological studies involving the nematode C. elegans, a common model organism. Here, we propose a framework for detecting worm-shaped objects in microscopic images that is based on convolutional neural networks (CNNs). We annotate nematodes with curved lines along the body, which is more suitable for worm-shaped objects than bounding boxes. The trained model predicts worm skeletons and body endpoints. The endpoints serve to untangle the skeletons from which segmentation masks are reconstructed by estimating the body width at each location along the skeleton. With light-weight backbone networks, we achieve 75.85 % precision, 73.02 % recall on a potato cyst nematode data set and 84.20 % precision, 85.63 % recall on a public C. elegans data set.
