用像素嵌入提升管状神经元分割精度，直接输出SWC结构树
Pixel Embedding Method for Tubular Neurite Segmentation
- 通过像素级嵌入向量区分重叠神经纤维，增强遮挡区域识别能力
- 在fMOST数据集上拓扑重建错误率显著低于经典方法
- 提出新拓扑评估指标，更准确衡量神经结构分割质量
自动分割神经元拓扑结构对处理大规模神经影像数据至关重要，可大幅加速神经元注释与分析。然而，神经元分支复杂形态及纤维间遮挡给基于深度学习的分割带来挑战。为此，我们提出改进框架：首先，设计输出像素级嵌入向量的深度网络，并构建相应损失函数，使学习特征能有效区分遮挡区域内的不同神经连接；其次，在该模型基础上构建端到端流水线，直接将原始神经图像映射为SWC格式的神经结构树；最后，针对现有评估指标无法充分捕捉分割精度的问题，提出新型拓扑评估指标，更合理量化神经元分割与重建质量。在fMOST成像数据集上的实验表明，相比多种经典方法，本方法显著降低神经元拓扑重建误差率。
原文摘要 · Abstract (English)
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.
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