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- yes (2)
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Document Type
- Journal article (2) (remove)
Language
- English (2)
Keywords
- cardiovascular magnetic resonance (CMR) (1)
- deep learning (1)
- flight behaviour (1)
- flupyradifurone (1)
- foraging (1)
- histology (1)
- honeybee (1)
- insecticide (1)
- mortality (1)
- radial (1)
Institute
- Theodor-Boveri-Institut für Biowissenschaften (2) (remove)
Purpose
Image acquisition and subsequent manual analysis of cardiac cine MRI is time-consuming. The purpose of this study was to train and evaluate a 3D artificial neural network for semantic segmentation of radially undersampled cardiac MRI to accelerate both scan time and postprocessing.
Methods
A database of Cartesian short-axis MR images of the heart (148,500 images, 484 examinations) was assembled from an openly accessible database and radial undersampling was simulated. A 3D U-Net architecture was pretrained for segmentation of undersampled spatiotemporal cine MRI. Transfer learning was then performed using samples from a second database, comprising 108 non-Cartesian radial cine series of the midventricular myocardium to optimize the performance for authentic data. The performance was evaluated for different levels of undersampling by the Dice similarity coefficient (DSC) with respect to reference labels, as well as by deriving ventricular volumes and myocardial masses.
Results
Without transfer learning, the pretrained model performed moderately on true radial data [maximum number of projections tested, P = 196; DSC = 0.87 (left ventricle), DSC = 0.76 (myocardium), and DSC =0.64 (right ventricle)]. After transfer learning with authentic data, the predictions achieved human level even for high undersampling rates (P = 33, DSC = 0.95, 0.87, and 0.93) without significant difference compared with segmentations derived from fully sampled data.
Conclusion
A 3D U-Net architecture can be used for semantic segmentation of radially undersampled cine acquisitions, achieving a performance comparable with human experts in fully sampled data. This approach can jointly accelerate time-consuming cine image acquisition and cumbersome manual image analysis.
1.Honeybees Apis mellifera and other pollinating insects suffer from pesticides in agricultural landscapes. Flupyradifurone is the active ingredient of a novel pesticide by the name of ‘Sivanto’, introduced by Bayer AG (Crop Science Division, Monheim am Rhein, Germany). It is recommended against sucking insects and marketed as ‘harmless’ to honeybees. Flupyradifurone binds to nicotinergic acetylcholine receptors like neonicotinoids, but it has a different mode of action. So far, little is known on how sublethal flupyradifurone doses affect honeybees.
2. We chronically applied a sublethal and field‐realistic concentration of flupyradifurone to test for long‐term effects on flight behaviour using radio‐frequency identification. We examined haematoxylin/eosin‐stained brains of flupyradifurone‐treated bees to investigate possible changes in brain morphology and brain damage.
3. A field‐realistic flupyradifurone dose of approximately 1.0 μg/bee/day significantly increased mortality. Pesticide‐treated bees initiated foraging earlier than control bees. No morphological damage in the brain was observed.
4. Synthesis and applications. The early onset of foraging induced by a chronical application of flupyradifurone could be disadvantageous for honeybee colonies, reducing the period of in‐hive tasks and life expectancy of individuals. Radio‐frequency identification technology is a valuable tool for studying pesticide effects on lifetime foraging behaviour of insects.