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- Title
DeepD3, an open framework for automated quantification of dendritic spines.
- Authors
Fernholz, Martin H. P.; Guggiana Nilo, Drago A.; Bonhoeffer, Tobias; Kist, Andreas M.
- Abstract
Dendritic spines are the seat of most excitatory synapses in the brain, and a cellular structure considered central to learning, memory, and activity-dependent plasticity. The quantification of dendritic spines from light microscopy data is usually performed by humans in a painstaking and error-prone process. We found that human-to-human variability is substantial (inter-rater reliability 82.2±6.4%), raising concerns about the reproducibility of experiments and the validity of using human-annotated 'ground truth' as an evaluation method for computational approaches of spine identification. To address this, we present DeepD3, an open deep learning-based framework to robustly quantify dendritic spines in microscopy data in a fully automated fashion. DeepD3's neural networks have been trained on data from different sources and experimental conditions, annotated and segmented by multiple experts and they offer precise quantification of dendrites and dendritic spines. Importantly, these networks were validated in a number of datasets on varying acquisition modalities, species, anatomical locations and fluorescent indicators. The entire DeepD3 open framework, including the fully segmented training data, a benchmark that multiple experts have annotated, and the DeepD3 model zoo is fully available, addressing the lack of openly available datasets of dendritic spines while offering a ready-to-use, flexible, transparent, and reproducible spine quantification method. Author summary: Our study approaches the automated quantification of a crucial structure in the brain: dendritic spines, critical to many functions of the brain such as learning and memory. Traditionally, scientists manually analyze these spines from microscope images, a process that is slow and prone to errors. We discovered that even among experts results vary substantially, thereby casting doubt on the reliability of such manual analyses. To overcome these challenges, we introduce DeepD3, an open-source tool that uses deep learning to automatically and accurately quantify dendritic spines. DeepD3 is unique because it is trained on a diverse range of data, annotated by multiple experts under various experimental conditions. This ensures its effectiveness across different species, brain regions, and imaging methods. By making DeepD3 freely available, we are providing researchers with a powerful tool that is not only efficient and reliable but also transparent and adaptable to different research needs. With DeepD3, we aim to improve consistency and reproducibility of neuroscience research involving dendritic spines to help disentangle the many complex functions of the brain, such as learning and memory.
- Subjects
DENDRITIC spines; FLUORESCENT probes; CELL anatomy; MICROSCOPY; DEEP learning; BRAIN anatomy
- Publication
PLoS Computational Biology, 2024, Vol 20, Issue 2, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1011774