MultiMorph: On-demand Atlas Construction

TL;DR

MultiMorph is a fast and efficient method for constructing anatomical atlases on the fly that 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.

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.

Figures (11)

• Figure 1: MultiMorph architecture diagram. The model takes in a variable group of $m$ images and constructs an atlas specific to that group. At each layer of the UNet, the proposed GroupBlock mechanism replaces standard convolution kernels. Specifically, it computes the elementwise mean of the intermediate features across the group, and concatenates the resulting features with the individual features. The mechanism enables group interaction by sharing summarized input features across the group. The network outputs $m$ velocity fields mapping images to the group-specific template space. A centrality layer removes any global bias in the average velocity field, before integration and warping the images. The output is a central template representing the shared anatomy of the input group.
• Figure 2: Example images and warps to the atlas constructed using the IXI dataset, for three subjects and three modalities.
• Figure 3: Atlases constructed on the IXI T1-w (left) and IXI PD-w (right) image modality. All baseline methods used the dataset for training or optimization, while our method was not trained on the IXI data. Further, our method was never trained on PD-w images, yet generalizes to this modality.
• Figure 4: Segmentation transfer performance when varying the number of images used to construct an atlas. Data is taken from the IXI T1-w dataset, which our model did not have access to during training. Our method consistently outperforms the baselines.
• Figure 5: Atlases conditioned on age for healthy subjects in OASIS-1. Ventricle enlargement (red boxes) is observed across time, consistent with neurodegeneration with aging.