Digital reconstruction of neuronal morphologies in 3D microscopy images is critical in the field of neuroscience. However, most existing automatic tracing algorithms cannot obtain accurate neuron reconstruction when processing 3D neuron images contaminated by strong background noises or containing weak filament signals. In this paper, we present a 3D neuron segmentation network named Structure-Guided Segmentation Network (SGSNet) to enhance weak neuronal structures and remove background noises. The network contains a shared encoding path but utilizes two decoding paths called Main Segmentation Branch (MSB) and Structure-Detection Branch (SDB), respectively. MSB is trained on binary labels to acquire the 3D neuron image segmentation maps. However, the segmentation results in challenging datasets often contain structural errors, such as discontinued segments of the weak-signal neuronal structures and missing filaments due to low signal-to-noise ratio (SNR). Therefore, SDB is presented to detect the neuronal structures by regressing neuron distance transform maps. Furthermore, a Structure Attention Module (SAM) is designed to integrate the multi-scale feature maps of the two decoding paths, and provide contextual guidance of structural features from SDB to MSB to improve the final segmentation performance. In the experiments, we evaluate our model in two challenging 3D neuron image datasets, the BigNeuron dataset and the Extended Whole Mouse Brain Sub-image (EWMBS) dataset. When using different tracing methods on the segmented images produced by our method rather than other state-of-the-art segmentation methods, the distance scores gain 42.48% and 35.83% improvement in the BigNeuron dataset and 37.75% and 23.13% in the EWMBS dataset.

中文翻译：

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用于 3D 神经元重建的结构引导分割


3D 显微图像中神经元形态的数字重建在神经科学领域至关重要。然而，大多数现有的自动跟踪算法在处理受强背景噪声污染或包含微弱灯丝信号的3D神经元图像时无法获得准确的神经元重建。在本文中，我们提出了一种名为结构引导分割网络（SGSNet）的 3D 神经元分割网络，以增强弱神经元结构并消除背景噪声。该网络包含共享编码路径，但使用两个解码路径，分别称为主分割分支（MSB）和结构检测分支（SDB）。 MSB 在二进制标签上进行训练，以获取 3D 神经元图像分割图。然而，具有挑战性的数据集的分割结果通常包含结构错误，例如弱信号神经元结构的不连续片段以及由于低信噪比（SNR）而丢失的细丝。因此，提出了 SDB 通过回归神经元距离变换图来检测神经元结构。此外，结构注意力模块（SAM）旨在集成两个解码路径的多尺度特征图，并提供从SDB到MSB的结构特征的上下文指导，以提高最终的分割性能。在实验中，我们在两个具有挑战性的 3D 神经元图像数据集（BigNeuron 数据集和扩展全小鼠大脑子图像 (EWMBS) 数据集）中评估我们的模型。当对我们的方法而不是其他最先进的分割方法生成的分割图像使用不同的跟踪方法时，BigNeuron 数据集中的距离分数提高了 42.48% 和 35.83%，EWMBS 数据集中的距离分数提高了 37.75% 和 23.13% 。