论文标题
通过测量路径预测对分离的树突棘的形态重建
Morphological Reconstruction of Detached Dendritic Spines via Geodesic Path Prediction
论文作者
论文摘要
荧光显微镜的树突状棘的形态重建是神经图像分析中的关键开放问题。现有的分割工具缺乏能力，无法处理长长，照明颈部膜的薄刺。我们解决了这个问题，并基于随机框架引入了一种无监督的路径预测技术，该框架从可能的脊柱颈部重建的路径空间中寻求最佳解决方案。我们的方法专门设计用于减少因异常值而造成的偏差，并且擅长重建受到噪音和差异不佳的图像的挑战形状。对两个光子显微镜数据的实验分析证明了我们方法的功效，其中在平均绝对重建误差方面观察到了比最新的12.5％的改善。
Morphological reconstruction of dendritic spines from fluorescent microscopy is a critical open problem in neuro-image analysis. Existing segmentation tools are ill-equipped to handle thin spines with long, poorly illuminated neck membranes. We address this issue, and introduce an unsupervised path prediction technique based on a stochastic framework which seeks the optimal solution from a path-space of possible spine neck reconstructions. Our method is specifically designed to reduce bias due to outliers, and is adept at reconstructing challenging shapes from images plagued by noise and poor contrast. Experimental analyses on two photon microscopy data demonstrate the efficacy of our method, where an improvement of 12.5% is observed over the state-of-the-art in terms of mean absolute reconstruction error.
