一键生成高精度脑图谱，速度比传统方法快100倍
MultiMorph: On-demand Atlas Construction
- 用前馈模型单次推理即生成群体特异性图谱
- 在小样本和大样本下均超越现有方法，提速100倍
- 无需调参即可适配新模态与人群，适合非专业研究者
我们提出MultiMorph，一种快速高效的即时解剖图谱构建方法。图谱能捕捉图像集合的典型结构，对量化人群间解剖变异至关重要。然而，现有图谱构建方法常需数日至数周计算，阻碍快速实验。因此，许多研究仍依赖不匹配人群的预设图谱，影响下游分析质量。MultiMorph通过前馈模型，在无需微调或优化的情况下，仅需一次前向传播即可为任意3D脑数据集生成高质量、群体特异性的图谱。该方法基于线性组交互层，实现输入图像组内特征聚合与共享；并通过引入辅助合成数据，在测试时泛化至新成像模态与人群。实验表明，MultiMorph在小样本与大样本场景下均优于当前最优的基于优化与学习的图谱构建方法，时间效率提升100倍。这使其成为无机器学习背景的生物医学研究者可便捷使用的框架，支持多样研究中的快速、高质量图谱生成。
原文摘要 · Abstract (English)
We present MultiMorph, a fast and efficient method for constructing anatomical atlases on the fly. Atlases capture the canonical structure of a collection of images and are essential for quantifying anatomical variability across populations. However, current atlas construction methods often require days to weeks of computation, thereby discouraging rapid experimentation. As a result, many scientific studies rely on suboptimal, precomputed atlases from mismatched populations, negatively impacting downstream analyses. MultiMorph addresses these challenges with a feedforward model that rapidly produces high-quality, population-specific atlases in a single forward pass for any 3D brain dataset, without any fine-tuning or optimization. MultiMorph is based on a linear group-interaction layer that aggregates and shares features within the group of input images. Further, by leveraging auxiliary synthetic data, MultiMorph generalizes to new imaging modalities and population groups at test-time. Experimentally, MultiMorph outperforms state-of-the-art optimization-based and learning-based atlas construction methods in both small and large population settings, with a 100-fold reduction in time. This makes MultiMorph an accessible framework for biomedical researchers without machine learning expertise, enabling rapid, high-quality atlas generation for diverse studies.
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