Thiyagarajan Vijay Venu, Sheridan Arlo, Harris Kristen M, Manor Uri
Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin Texas, 78712.
Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, 92037.
Res Sq. 2024 Nov 14:rs.3.rs-5339143. doi: 10.21203/rs.3.rs-5339143/v1.
Producing dense 3D reconstructions from biological imaging data is a challenging instance segmentation task that requires significant ground-truth training data for effective and accurate deep learning-based models. Generating training data requires intense human effort to annotate each instance of an object across serial section images. Our focus is on the especially complicated brain neuropil, comprising an extensive interdigitation of dendritic, axonal, and glial processes visualized through serial section electron microscopy. We developed a novel deep learning-based method to generate dense 3D segmentations rapidly from sparse 2D annotations of a few objects on single sections. Models trained on the rapidly generated segmentations achieved similar accuracy as those trained on expert dense ground-truth annotations. Human time to generate annotations was reduced by three orders of magnitude and could be produced by non-expert annotators. This capability will democratize generation of training data for large image volumes needed to achieve brain circuits and measures of circuit strengths.
从生物成像数据生成密集的三维重建是一项具有挑战性的实例分割任务，对于有效且准确的基于深度学习的模型而言，需要大量的真实训练数据。生成训练数据需要投入大量人力来标注连续切片图像中的每个物体实例。我们关注的是特别复杂的脑神经网络，它由通过连续切片电子显微镜观察到的树突、轴突和胶质细胞突起广泛交错组成。我们开发了一种基于深度学习的新方法，可根据单一切片上少数物体的稀疏二维标注快速生成密集的三维分割。在快速生成的分割上训练的模型与在专家密集真实标注上训练的模型具有相似的准确性。生成标注所需的人力时间减少了三个数量级，并且非专业标注人员也可以完成。这种能力将使生成用于实现脑回路和回路强度测量所需的大量图像的训练数据变得更加普及。
