The main olfactory bulb is the key element of the olfactory pathway of rodents. To precisely dissect the neural pathway in the main olfactory bulb (MOB), it is necessary to construct the three-dimensional morphologies of the anatomical structures within it with micro-level resolution. However, the construction remains challenging due to the complicated shape of the anatomical structures in the main olfactory bulb and the high resolution of micro-optical images. To address these issues, we propose an interactive volume image segmentation method with micro-level resolution in the horizontal and axial direction. Firstly, we obtain the initial location of the anatomical structures by manual annotation and design a patch-based neural network to learn the complex texture feature of the anatomical structures. Then we randomly sample some patches to predict by the trained network and perform an annotation reconstruction based on intensity calculation to get the final location results of the anatomical structures. Our experiments were conducted using Nissl-stained brain images acquired by the Micro-optical sectioning tomography (MOST) system. Our method achieved a mean dice similarity coefficient (DSC) of 81.8% and obtain the best segmentation performance. At the same time, the experiment shows the three-dimensional morphology reconstruction results of the anatomical structures in the main olfactory bulb are smooth and consistent with their natural shapes, which addresses the possibility of constructing three-dimensional morphologies of the anatomical structures in the whole brain.

中文翻译：

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一种微分辨率主嗅球解剖结构交互式图像分割方法

主嗅球是啮齿动物嗅觉通路的关键元件。为了精确解剖主嗅球（MOB）的神经通路，需要以微观分辨率构建其内部解剖结构的三维形态。然而，由于主嗅球解剖结构的复杂形状和显微光学图像的高分辨率，构建仍然具有挑战性。为了解决这些问题，我们提出了一种在水平和轴向方向具有微观分辨率的交互式体积图像分割方法。首先，我们通过手动标注获得解剖结构的初始位置，并设计基于块的神经网络来学习解剖结构的复杂纹理特征。然后，我们随机采样一些补丁，通过训练后的网络进行预测，并基于强度计算进行注释重建，以获得解剖结构的最终定位结果。我们的实验是使用微光学断层扫描（MOST）系统获取的尼氏染色脑图像进行的。我们的方法实现了 81.8% 的平均骰子相似系数 (DSC)，并获得了最佳的分割性能。同时，实验表明主嗅球解剖结构三维形态重建结果光滑且与自然形状一致，解决了整体构建解剖结构三维形态的可能性。脑。