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

arXiv: https://arxiv.org/abs/2004.09795 · HTML: https://mlsanity.com/paper/2004.09795/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [NemaNet: A convolutional neural network model for identification of nematodes soybean crop in brazil](https://mlsanity.com/paper/2103.03717/) (2021)
- [I-Nema: A Biological Image Dataset for Nematode Recognition](https://mlsanity.com/paper/2103.08335/) (2021)
- [High-throughput Phenotyping of Nematode Cysts](https://mlsanity.com/paper/2110.07057/) (2021)
- [Quantifying Nematodes through Images: Datasets, Models, and Baselines of Deep Learning](https://mlsanity.com/paper/2404.19748/) (2024)

## Abstract (arXiv)

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.