发表机构
University of Regensburg（雷根斯堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种基于深度学习的果蝇幼虫大脑体积配准流程，实现单次前向传播的高分辨率变形对齐，在精度和速度上均优于经典方法，并开源了完整框架。
AI 中文摘要
黑腹果蝇的幼虫阶段是神经科学的一个紧凑模型系统，其遗传工具允许在特定的神经群体中表达荧光标记物，而跨动物比较所产生的表达模式要求将每个大脑配准到一个共享的解剖参考空间。现有的此类任务流程主要基于经典配准方法，这些方法对每个体积执行新的优化，通常需要针对具体情况进行参数调整，并且每个大脑可能需要数分钟，这限制了它们作为常规预处理步骤的使用。我们提出了一种经过训练的深度配准流程，该流程在单个前向传播中以高空间分辨率将幼虫大脑变形对齐到参考模板，处理的体积体素数量比通常报道的学习式三维配准多出数倍，同时提供了应用该流程所需的预处理和基于解剖标志的评估流程。在包含不同采集和质量层次的留出集上，与十一种经典方法和七种进一步的学习式基线相比，所提出的流程最为准确，在解剖标志局部互信息上比最强的经典基线提高了23个百分点。它配准一个体积的速度比经典可变形流程快一到两个数量级，并且在采集质量下降时，它比其他任何方法都保留了更多的准确性。该网络、其训练权重和完整流程作为开源深度幼虫大脑配准框架发布：此 https URL
英文摘要
The larval stage of Drosophila melanogaster is a compact model system for neuroscience whose genetic toolkit allows fluorescent markers to be expressed in defined neural populations, and comparing the resulting expression patterns across animals requires every brain to be registered into a shared anatomical reference space. Existing pipelines for this task are predominantly based on classical registration methods, which perform a new optimization for each volume, often require per-case parameter tuning, and can take minutes per brain, limiting their use as a routine preprocessing step. We present a trained deep registration pipeline that deformably aligns a larval brain to a reference template in a single forward pass at high spatial resolution, on volumes that hold several times more voxels than those learned 3D registration is normally reported on, together with the preprocessing and anatomy-anchored evaluation pipeline required to apply it. Against eleven classical and seven further learned baselines on a held-out collection acquired with different acquisition and quality strata, the proposed pipeline is the most accurate, improving on the strongest classical baseline by 23 percentage points of anatomical landmark-local mutual information. It registers a volume one to two orders of magnitude faster than the classical deformable pipelines, and it retains more of its accuracy than any other method as acquisition quality degrades. The network, its trained weights and the full pipeline are released as the open-source deep larval brain registration framework: https://github.com/agentdr1/deep-larval-brain-reg
