Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China.
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China; School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, China.
Comput Methods Programs Biomed. 2022 Jun;219:106759. doi: 10.1016/j.cmpb.2022.106759. Epub 2022 Mar 15.
The goal of micro-connectomics research is to reconstruct the connectome and elucidate the mechanisms and functions of the nervous system via electron microscopy (EM). Due to the enormous variety of neuronal structures, neuron segmentation is among most difficult tasks in connectome reconstruction, and neuroanatomists desperately need a reliable neuronal structure segmentation method to reduce the burden of manual labeling and validation.
In this article, we proposed an effective deep learning method based on a deep residual contextual and subpixel convolution network to obtain the neuronal structure segmentation in anisotropic EM image stacks. Furthermore, lifted multicut is used for post-processing to optimize the prediction and obtain the reconstruction results.
On the ISBI EM segmentation challenge, the proposed method ranks among the top of the leader board and yields a Rand score of 0.98788. On the public data set of mouse piriform cortex, it achieves a Rand score of 0.9562 and 0.9318 in the different testing stacks. The evaluation scores of our method are significantly improved when compared with those of state-of-the-art methods.
The proposed automatic method contributes to the development of micro-connectomics, which improves the accuracy of neuronal structure segmentation and provides neuroanatomists with an effective approach to obtain the segmentation and reconstruction of neurons.
微观连接组学研究的目标是通过电子显微镜（EM）重建连接组并阐明神经系统的机制和功能。由于神经元结构的巨大多样性，神经元分割是连接组重建中最困难的任务之一，神经解剖学家迫切需要一种可靠的神经元结构分割方法来减少手动标记和验证的负担。
本文提出了一种基于深度残差上下文和子像素卷积网络的有效深度学习方法，用于获得各向异性 EM 图像堆栈中的神经元结构分割。此外，使用提升多切割进行后处理以优化预测并获得重建结果。
在 ISBI EM 分割挑战赛上，所提出的方法在排行榜上名列前茅，Rand 得分为 0.98788。在公共的小鼠梨状皮层数据集上，在不同的测试堆栈中，Rand 得分为 0.9562 和 0.9318。与最先进的方法相比，我们的方法的评估得分有了显著提高。
所提出的自动方法有助于微观连接组学的发展，提高了神经元结构分割的准确性，并为神经解剖学家提供了一种有效的方法来获得神经元的分割和重建。
