MultiMorph: On-demand Atlas Construction

2025-03-31

AI Summary

Current anatomical atlas construction relies on time-consuming optimization procedures lasting days to weeks, compelling researchers to adopt precomputed atlases derived from mismatched populations—thereby compromising analysis accuracy. MultiMorph introduces an instantaneous atlas generation paradigm: it produces high-fidelity, population-specific 3D brain image group atlases via a single forward pass, eliminating the need for fine-tuning or iterative optimization. Its core innovation is the first-of-its-kind linear group interaction layer, enabling intra-group feature aggregation and sharing; integrated with a feedforward network architecture and synthetic data distillation, it achieves zero-shot generalization to novel imaging modalities and populations. Experiments demonstrate that MultiMorph accelerates atlas generation by 100× over state-of-the-art methods while consistently improving atlas quality across diverse population scales. This enables non-machine-learning experts to deploy high-precision atlas generation efficiently and robustly.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

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.

Problem

• Rapid construction of anatomical atlases for diverse populations
• Overcoming slow computation in current atlas construction methods
• Enabling high-quality atlas generation without machine learning expertise

Innovation

• Feedforward model for instant atlas construction
• Linear group-interaction layer for feature sharing
• Synthetic data for cross-modality generalization