DeepD3, an open framework for automated quantification of dendritic spines

Fernholz MH, Guggiana Nilo DA, Bonhoeffer T, Kist A (2024)

Publication Type: Journal article

Publication year: 2024

Journal

PLoS Computational Biology Public Library of Science

Book Volume: 20

Article Number: e1011774

Journal Issue: 2

DOI: 10.1371/journal.pcbi.1011774

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.

Authors with CRIS profile

Andreas Kist Juniorprofessur für Artificial Intelligence in Communication Disorders

Involved external institutions

Max-Planck-Institut für biologische Intelligenz DE Germany (DE)

How to cite

APA:

Fernholz, M.H., Guggiana Nilo, D.A., Bonhoeffer, T., & Kist, A. (2024). DeepD3, an open framework for automated quantification of dendritic spines. PLoS Computational Biology, 20(2). https://doi.org/10.1371/journal.pcbi.1011774

MLA:

Fernholz, Martin H.P., et al. "DeepD3, an open framework for automated quantification of dendritic spines." PLoS Computational Biology 20.2 (2024).

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