Topic: DeepD3

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. Typically, humans quantify dendritic spines from light microscopy data in a manual painstaking, 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 pres

Automatic methods for dendrite and dendritic spine segmentation are important for high-throughput neuroanatomical datasets. Assessing their performance compared to human annotators and ideally the real underlying neuroanatomy is crucial to determine overall reliability of those methods. However, most datasources, for example in two-photon microscopy, do not even allow identifying dendritic spines as an ultimate ground truth because of optical resolution limits. Here, we introduce synthetic microscopy to generate neuroanatomy stacks using a combination of random walk dendritic growth and random