标题： 管状神经元突起分割的像素嵌入方法 显示英文标题

标题： Pixel Embedding Method for Tubular Neurite Segmentation

摘要： 自动分割神经元拓扑结构对于处理大规模神经成像数据至关重要，因为它可以大大加速神经元注释和分析。 然而，神经元分支的复杂形态以及纤维之间的遮挡给基于深度学习的分割带来了重大挑战。 为了解决这些问题，我们提出了一种改进的框架：首先，我们引入了一个输出像素级嵌入向量的深度网络，并设计了一个相应的损失函数，使学习到的特征能够有效区分遮挡区域内的不同神经元连接。 其次，在此模型的基础上，我们开发了一个端到端的流程，直接将原始神经元图像映射到SWC格式的神经元结构树。 最后，认识到现有的评估指标无法完全捕捉分割精度，我们提出了一种新的拓扑评估指标，以更准确地量化神经元分割和重建的质量。 在我们的fMOST成像数据集上的实验表明，与几种经典方法相比，我们的方法显著降低了神经元拓扑结构重建的错误率。 显示英文摘要

摘要： Automatic segmentation of neuronal topology is critical for handling large scale neuroimaging data, as it can greatly accelerate neuron annotation and analysis. However, the intricate morphology of neuronal branches and the occlusions among fibers pose significant challenges for deep learning based segmentation. To address these issues, we propose an improved framework: First, we introduce a deep network that outputs pixel level embedding vectors and design a corresponding loss function, enabling the learned features to effectively distinguish different neuronal connections within occluded regions. Second, building on this model, we develop an end to end pipeline that directly maps raw neuronal images to SWC formatted neuron structure trees. Finally, recognizing that existing evaluation metrics fail to fully capture segmentation accuracy, we propose a novel topological assessment metric to more appropriately quantify the quality of neuron segmentation and reconstruction. Experiments on our fMOST imaging dataset demonstrate that, compared to several classical methods, our approach significantly reduces the error rate in neuronal topology reconstruction.