Instance Segmentation of Nematode Cysts in Microscopic Images of Soil Samples
| Autor: | Hans-Georg Luigs, Matthias Daub, Dorit Merhof, Marcus Jansen, Martin Strauch, Long Chen |
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| Rok vydání: | 2020 |
| Předmět: |
Support Vector Machine
Soil test Nematoda Computer science business.industry Detector Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Image segmentation 030218 nuclear medicine & medical imaging Data set 03 medical and health sciences Soil 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Animals 020201 artificial intelligence & image processing Segmentation Artificial intelligence Precision and recall business Algorithms |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | Nematodes are plant parasites that cause damage to crops. In order to quantify nematode infestation based on soil samples, we propose an instance segmentation method that will serve as the basis of automatic quantitative analysis. We consider light microscopic images of cluttered object collections as they occur in realistic soil samples. We introduce an algorithm, LMBI (Local Maximum of Boundary Intensity) to propose instance segmentation candidates. In a second step, a SVM classifier separates the nematode cysts among the candidates from soil particles. On a data set of soil sample images, the LMBI detector achieves near-optimal recall with a limited number of candidate segmentations, and the combined detector/classifier achieves recall and precision of 0.7. The pipeline only requires simple dot annotations and ≈moderately sized training data, which enables quick annotating and training in laboratory applications. |
| Databáze: | OpenAIRE |
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