School of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan, China.
Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
Neuroinformatics. 2022 Oct;20(4):1155-1167. doi: 10.1007/s12021-022-09594-3. Epub 2022 Jul 19.
Neuron reconstruction can provide the quantitative data required for measuring the neuronal morphology and is crucial in brain research. However, the difficulty in reconstructing dense neurites, wherein massive labor is required for accurate reconstruction in most cases, has not been well resolved. In this work, we provide a new pathway for solving this challenge by proposing the super-resolution segmentation network (SRSNet), which builds the mapping of the neurites in the original neuronal images and their segmentation in a higher-resolution (HR) space. During the segmentation process, the distances between the boundaries of the packed neurites are enlarged, and only the central parts of the neurites are segmented. Owing to this strategy, the super-resolution segmented images are produced for subsequent reconstruction. We carried out experiments on neuronal images with a voxel size of 0.2 μm × 0.2 μm × 1 μm produced by fMOST. SRSNet achieves an average F1 score of 0.88 for automatic packed neurites reconstruction, which takes both the precision and recall values into account, while the average F1 scores of other state-of-the-art automatic tracing methods are less than 0.70.
神经元重建可以提供测量神经元形态所需的定量数据，在大脑研究中至关重要。然而，密集神经突的重建一直是一个难题，在大多数情况下，需要大量的劳动力才能实现准确的重建。在这项工作中，我们提出了一种新的方法，即超分辨率分割网络（SRSNet），通过该方法来解决这个挑战。该方法构建了原始神经元图像中的神经突及其在更高分辨率（HR）空间中的分割之间的映射。在分割过程中，会扩大密集神经突边界之间的距离，并且仅分割神经突的中心部分。由于这种策略，可以产生用于后续重建的超分辨率分割图像。我们在 fMOST 生成的体素大小为 0.2μm×0.2μm×1μm 的神经元图像上进行了实验。SRSNet 实现了 0.88 的平均 F1 分数，用于自动重建密集神经突，同时考虑了精度和召回率，而其他最先进的自动追踪方法的平均 F1 分数都小于 0.70。
