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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, acommon model organism. Here, we propose a framework for detecting worm-shapedobjects in microscopic images that is based on convolutional neural networks(CNNs). We annotate nematodes with curved lines along the body, which is moresuitable for worm-shaped objects than bounding boxes. The trained modelpredicts worm skeletons and body endpoints. The endpoints serve to untangle theskeletons from which segmentation masks are reconstructed by estimating thebody width at each location along the skeleton. With light-weight backbonenetworks, we achieve 75.85 % precision, 73.02 % recall on a potato cystnematode data set and 84.20 % precision, 85.63 % recall on a public C. elegansdata set.

A CNN Framenwork Based on Line Annotations for Detecting Nematodes in Microscopic Images