Modeling 3D mesoscaled neuronal complexity through learning-based dynamic morphometric convolution

doi:10.1186/s40708-025-00288-5

2026-01-29

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DOI：10.1186/s40708-025-00288-5

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从三维（3D）光显微成像中精确重建神经元形态是神经科学的基础。然而，神经元树突本质上表现出细长、扭曲的几何形状，且具有高方向变异性，这对标准3D卷积构成了重大挑战，因为其静态、轴对齐的感受野缺乏适应此类复杂形态的能力。为解决这一问题，我们提出了动态形态感知卷积（DMAC）框架，该框架通过同时调整卷积核的形状和方向，将固有的几何先验融入卷积中。这实现了针对树突化和方向可变的神经元轨迹的形态感知特征提取。具体而言，我们首先应用动态管状卷积来弥合各向同性卷积核与神经元细长形态之间的结构不匹配。为充分适应神经元分支的3D方向变异性，我们进一步引入一种旋转机制，通过两个可学习的角度（仰角和方位角）动态地重新定向管状核，从而实现与局部神经元方向的精确对齐。我们在四个中尺度神经元成像数据集上进行了广泛的实验验证，其中包括来自BigNeuron项目（果蝇和小鼠）的两个数据集以及两个额外基准（NeuroFly和CWMBS）。我们的方法在各项指标上均优于当前最佳方法，在整体结构平均（ESA）、不同结构平均（DSA）和不同结构百分比（PDS）上分别实现了5.4%、6.9%和7.5%的平均提升。这些结果证明了我们提出的DMAC在捕捉复杂形态变化和增强结构保真度方面的有效性，尤其是在多样化的中尺度神经元形态中。

Keyword:

Neuron reconstruction

Image segmentation

Microscopy images

Morphological learning

期刊

Brain Informatics

IF:

4.5

论文数:

被引数:

903

机构

university of sydney

学者数:

6.4K

论文数: 2.9K

被引数: 0

fudan university

学者数:

11.8W

论文数: 7.7W

被引数: 121

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