A CNN Framework Based on Line Annotations for Detecting Nematodes in Microscopic Images
| Autor: | Marcus Jansen, Martin Strauch, Xiaochen Jiang, Long Chen, Dorit Merhof, Hans-Georg Luigs, Matthias Daub, Stefan Krüssel, Susanne Schultz-Kuhlmann |
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| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
Biological studies biology business.industry Computer science Pattern recognition Potato cyst nematode biology.organism_classification Convolutional neural network Image (mathematics) Data set 03 medical and health sciences 0302 clinical medicine Nematode Line (geometry) Artificial intelligence business Scale (map) 030217 neurology & neurosurgery 030304 developmental biology |
| Zdroj: | ISBI |
| DOI: | 10.1109/isbi45749.2020.9098465 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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