论文标题
基于深度学习的神经元重建的质量控制
Quality Control of Neuron Reconstruction Based on Deep Learning
论文作者
论文摘要
神经元重建对于生成精致的神经元连接图至关重要，以了解大脑功能。尽管对自动重建方法产生了很大的影响，但仍需要训练有素的人类注释者手动追踪。为了确保重建神经元的质量并为注释者提供指导以提高其效率，我们在本文中提出了一种基于学习的基于学习的质量控制方法。通过将质量控制问题提出为每个点的二进制分类任务，提出的方法克服了较大的图像大小和复杂的神经元形态所带来的技术困难。它不仅提供了重建质量的评估，而且还可以准确地定位错误的跟踪开始的位置。这项工作介绍了神经元重建的全脑尺度质量控制的首批综合研究之一。对五倍的交叉验证的实验与大型数据集进行了表明，所提出的方法可以检测74.7％的错误，仅1.4％的错误警报。
Neuron reconstruction is essential to generate exquisite neuron connectivity map for understanding brain function. Despite the significant amount of effect that has been made on automatic reconstruction methods, manual tracing by well-trained human annotators is still necessary. To ensure the quality of reconstructed neurons and provide guidance for annotators to improve their efficiency, we propose a deep learning based quality control method for neuron reconstruction in this paper. By formulating the quality control problem into a binary classification task regarding each single point, the proposed approach overcomes the technical difficulties resulting from the large image size and complex neuron morphology. Not only it provides the evaluation of reconstruction quality, but also can locate exactly where the wrong tracing begins. This work presents one of the first comprehensive studies for whole-brain scale quality control of neuron reconstructions. Experiments on five-fold cross validation with a large dataset demonstrate that the proposed approach can detect 74.7% errors with only 1.4% false alerts.
