Neuronal shape reconstruction is a helpful technique for establishing neuron identity, inferring neuronal connections, mapping neuronal circuits, and so on. Advances in optical imaging techniques have enabled data collection that includes the shape of a neuron across the whole brain, considerably extending the scope of neuronal anatomy. However, such datasets often include many fuzzy neurites and many crossover regions that neurites are closely attached, which make neuronal shape reconstruction more challenging. In this study, we proposed a convex image segmentation model for neuronal shape reconstruction that segments a neurite into cross sections along its traced skeleton. Both the sparse nature of gradient images and the rule that fuzzy neurites usually have a small radius are utilized to improve neuronal shape reconstruction in regions with fuzzy neurites. Because the model is closely related to the traced skeleton point, we can use this relationship for identifying neurite with crossover regions. We demonstrated the performance of our model on various datasets, including those with fuzzy neurites and neurites with crossover regions, and we verified that our model could robustly reconstruct the neuron shape on a brain-wide scale.

中文翻译：

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使用凸图像分割方法对跟踪的神经元进行全脑形状重建。

神经元形状重建是建立神经元身份，推断神经元连接，映射神经元回路等的有用技术。光学成像技术的进步使得能够进行包括整个大脑神经元形状在内的数据收集，从而大大扩展了神经元解剖结构的范围。但是，此类数据集通常包含许多模糊神经突和神经突紧密相连的许多交叉区域，这使得神经元形状重建更具挑战性。在这项研究中，我们提出了一种用于神经元形状重建的凸图像分割模型，该模型将神经突沿着其所跟踪的骨骼分割为多个横截面。梯度图像的稀疏性质和模糊神经突通常具有较小半径的规则均被用于改善具有模糊神经突的区域中的神经元形状重建。由于模型与跟踪的骨架点密切相关，因此我们可以使用这种关系来识别具有交叉区域的神经突。我们证明了我们的模型在各种数据集上的性能，包括具有模糊神经突和具有交叉区域的神经突的数据集，并且我们验证了我们的模型可以在脑范围内稳健地重建神经元形状。