Filtern
Volltext vorhanden
- ja (18)
Gehört zur Bibliographie
- ja (18)
Dokumenttyp
Sprache
- Englisch (18)
Schlagworte
- pain (2)
- Alzheimers disease (1)
- Amyotrophic-lateral-sclerosis (1)
- Associative learning (1)
- Axon degeneration (1)
- Axon growth (1)
- Axonal transport (1)
- BDNF (1)
- BSTA (1)
- Brain (1)
- CD133 (1)
- Ca2+ homeostasis (1)
- Ca2+ ion analysis (1)
- Ca2+ leak (1)
- Ca2+ oscillation (1)
- Cells (1)
- Drosophilia (1)
- ER Ca2+ imaging (1)
- ER Ca2+ store (1)
- Fear conditioning (1)
- Glioblastoma (1)
- Glioma stem cells (1)
- Glutamatergic synapses (1)
- Intermediate filaments (1)
- Lacking neurofilaments (1)
- Microtubules (1)
- Missense mutation (1)
- Motoneuron disease (1)
- Motor behaviour (1)
- Mouse model (1)
- NaV1.9 (1)
- Nestin (1)
- Neurofilament (1)
- Neurotrophic factors (1)
- Object recognition (1)
- Osteopontin (1)
- PKB/Akt phosphorylation (1)
- Parkinson’s disease (1)
- Phosphorylation (1)
- Plasma-membrane (1)
- Pleckstrin homology containing family member 5 (Plekhg5) (1)
- Progressive motor neuronopathy (1)
- Protein kinase B (1)
- Rictor-mTOR complex (1)
- SAP47 gene (1)
- SERCA (1)
- Spinal Muscular-arthropy (1)
- Stat3 (1)
- Stathmin (1)
- Syap1 knockout (1)
- Syap1 localization (1)
- T-cadherin (1)
- TRP channel (1)
- Transgenic mice (1)
- Viability (1)
- alpha-7 nicotinic acetylcholine receptor (1)
- analgesia (1)
- anxiety (1)
- autophagy (1)
- axon growth (1)
- barrier (1)
- cadherin-13 (CDH13) (1)
- cerebellum (1)
- choline acetyltransferase (1)
- chronic pain (1)
- claudin-5 (1)
- deep brain stimulation (1)
- dentate gyrus (1)
- dorsal raphe (1)
- dorsal root ganglion (1)
- embryos (1)
- fear (1)
- glycine receptor (1)
- granule cells (1)
- hippocampal neurons (1)
- hippocampus (1)
- ion channel (1)
- ion channels in the nervous system (1)
- local protein synthesis (1)
- machine learning (1)
- macrophages (1)
- mesencephalic locomotor region (1)
- microscopy (1)
- mimetic peptide (1)
- molecular medicine (1)
- motoneuron disease (1)
- motoneurons (1)
- nerve injury (1)
- neurodegeneration (1)
- neurodevelopment (1)
- neuroinflammation (1)
- neuronal dendrites (1)
- neuronal differentiation (1)
- neurons (1)
- neuropathic pain (1)
- neuropathy (1)
- nociception (1)
- oxidized phospholipids (1)
- pain therapy (1)
- photothrombotic stroke (1)
- prefrontal cortex (1)
- presynapse (1)
- psychiatric disorders (1)
- quality control (1)
- radial glia (1)
- regulation (1)
- serotonin (1)
- sodium channel (1)
- software (1)
- spastic (1)
- spinal muscular atrophy (1)
- spontaneous excitation (1)
- startle reaction (1)
- store-operated Ca2+ entry (1)
- synapse structure (1)
- synaptic localization (1)
- synaptic vesicles (1)
- therapeutic antibody (1)
- tight junction (1)
- α-synuclein-specific T cells (1)
Institut
- Institut für Klinische Neurobiologie (16)
- Neurologische Klinik und Poliklinik (7)
- Klinik und Poliklinik für Anästhesiologie (ab 2004) (5)
- Institut für Anatomie und Zellbiologie (3)
- Theodor-Boveri-Institut für Biowissenschaften (3)
- Klinik und Poliklinik für Psychiatrie, Psychosomatik und Psychotherapie (2)
- Pathologisches Institut (2)
- Betriebswirtschaftliches Institut (1)
- Institut für Experimentelle Biomedizin (1)
- Institut für Medizinische Strahlenkunde und Zellforschung (1)
Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the training, evaluation, and application of deep learning models on ambiguous data. The tool’s training and evaluation pipeline uses multiple expert annotations and deep model ensembles to achieve accurate results. The application pipeline supports various use-cases for expert annotations and includes a quality assurance mechanism in the form of uncertainty measures. Benchmarked against other tools, deepflash2 offers both high predictive accuracy and efficient computational resource usage. The tool is built upon established deep learning libraries and enables sharing of trained model ensembles with the research community. deepflash2 aims to simplify the integration of deep learning into bioimage analysis projects while improving accuracy and reliability.