Frankfurt Institute for Advanced Studies and Department of Computer Science and Mathematics, Goethe University Frankfurt, Ruth-Moufang-Straße 1, 60438, Frankfurt am Main, Germany.
Institute for Cell Biology and Neuroscience, Goethe University Frankfurt, Max-von-Laue-Str. 15, 60438, Frankfurt am Main, Germany.
Sci Rep. 2023 Nov 22;13(1):20497. doi: 10.1038/s41598-023-47070-3.
Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to simultaneously image large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for 3D spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data acquired by two-photon imaging in vivo. The core of our pipeline is a deep convolutional neural network that was pretrained on a general-purpose image library and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labeled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection reaching a performance slightly below that of the human experts. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters.
树突棘被认为是兴奋性突触的形态学代表，因此成为许多不同研究方向的目标。近年来，已经可以在神经组织的三维体积中同时对大量的树突棘进行成像。相比之下，目前还没有一种自动化的三维树突棘检测方法能够接近人类专家所达到的检测性能。然而，要利用这些数据集，就需要新的工具来对大量的树突棘进行全自动检测和分析。在这里，我们开发了一种高效的分析流水线，用于检测在体内双光子成像中获取的容积荧光成像数据中的大量树突棘。我们的流水线的核心是一个深度卷积神经网络，该网络是在通用图像库上进行预训练的，然后在树突棘检测任务上进行优化。这种迁移学习方法在数据效率高的同时，还能达到高精度的检测效果。为了训练和验证该模型，我们使用了五个人类专家注释者来生成一个标记数据集，以考虑到人类树突棘检测的可变性。该流水线可以实现全自动的树突棘检测，其性能略低于人类专家。我们的树突棘检测方法快速、准确且稳健，因此非常适合具有数千个树突棘的大规模数据集。该代码很容易适用于新的数据集，即使没有任何重新训练或调整模型参数，也能达到很高的检测性能。
