论文标题
3D轴突分割的自学特征提取
Self-Supervised Feature Extraction for 3D Axon Segmentation
论文作者
论文摘要
在3D脑图像中自动跟踪轴突的现有基于学习的方法通常依赖于手动注释的分段标签。标签是一个劳动密集型过程，不可扩展到全脑分析，这是提高对大脑功能的理解所需的。我们提出了一个自制的辅助任务，该任务利用轴突的管子结构从未标记的数据中构建特征提取器。所提出的辅助任务限制了3D卷积神经网络（CNN），以预测输入3D体积中排列的切片的顺序。通过解决此任务，3D CNN能够学习功能，而无需接地标签，这些标签可用于使用3D U-NET模型下游分割。据我们所知，我们的模型是第一个使用盾牌技术以亚细胞分辨率成像的轴突进行自动分割的模型。我们证明了Shield PVGPE数据集和Bigneuron项目，单个神经元Janelia数据集的3D U-NET模型的细分性能的改进。
Existing learning-based methods to automatically trace axons in 3D brain imagery often rely on manually annotated segmentation labels. Labeling is a labor-intensive process and is not scalable to whole-brain analysis, which is needed for improved understanding of brain function. We propose a self-supervised auxiliary task that utilizes the tube-like structure of axons to build a feature extractor from unlabeled data. The proposed auxiliary task constrains a 3D convolutional neural network (CNN) to predict the order of permuted slices in an input 3D volume. By solving this task, the 3D CNN is able to learn features without ground-truth labels that are useful for downstream segmentation with the 3D U-Net model. To the best of our knowledge, our model is the first to perform automated segmentation of axons imaged at subcellular resolution with the SHIELD technique. We demonstrate improved segmentation performance over the 3D U-Net model on both the SHIELD PVGPe dataset and the BigNeuron Project, single neuron Janelia dataset.
