基于测地路径预测的分离树突棘形态重建
Morphological Reconstruction of Detached Dendritic Spines via Geodesic Path Prediction
AI总结：
针对现有分割工具难以重建光照不足的细长树突棘颈部的问题，提出基于随机框架的无监督测地路径预测方法，在双光子显微镜数据上的平均重建误差较SOTA降低12.5%。
摘要：
从荧光显微镜图像中进行树突棘的形态重建是神经图像分析中一个关键的开放性问题。现有的分割工具难以处理带有长且光照不足的颈部膜的纤细树突棘。针对这一问题，我们提出了一种基于随机框架的无监督路径预测技术，该技术从可能的树突棘颈部重建的路径空间中寻找最优解。我们的方法专门用于降低由离群值导致的偏差，擅长从受噪声和低对比度困扰的图像中重建具有挑战性的形状。在双光子显微镜数据上的实验分析证明了该方法的有效性，在平均绝对重建误差方面，其性能较当前最优方法提升了12.5%。
英文摘要：
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.
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