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

IEEE International Symposium on Biomedical Imaging (ISBI), pp. 508–512

Abstract

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.

Authors 9

1. Long Chen Aachen Imaging and Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

2. Martin Strauch Aachen Imaging and Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

3. Matthias Daub

   Julius Kühn-Institut

   Affiliation as printed

   Julius Kühn Institute: Federal Research Centre for Cultivated Plants, Elsdorf, Germany

4. Xiaochen Jiang Aachen Imaging and Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

5. Marcus Jansen
   Affiliation as printed

   LemnaTec GmbH, Aachen, Germany

6. Hans-Georg Luigs
   Affiliation as printed

   LemnaTec GmbH, Aachen, Germany

7. Susanne Schultz-Kuhlmann

   Landwirtschaftskammer Niedersachsen

   Affiliation as printed

   Landwirtschaftskammer Niedersachsen - Pflanzenschutzamt (PSA), Hannover, Germany

8. Stefan Krüssel

   Landwirtschaftskammer Niedersachsen

   Affiliation as printed

   Landwirtschaftskammer Niedersachsen - Pflanzenschutzamt (PSA), Hannover, Germany

9. Dorit Merhof Aachen Imaging and Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany