Fast segmentation with the NextBrain histological atlas

Abstract

Structural brain analysis at the subregion level offers critical insights into healthy aging and neurodegenerative diseases. The NextBrain histological atlas was recently introduced to support such fine-grained investigations, but its existing Bayesian segmentation framework remains computationally prohibitive, particularly for large-scale studies. We present a new, open-source tool that dramatically accelerates segmentation using a hybrid approach combining: machine learning, contrast-adaptive segmentation; target-specific image synthesis; and fast diffeomorphic registration (all three with GPU support). Our method enables highly granular segmentation of brain MRI scans of any resolution and contrast (in vivo or ex vivo) at a fraction of the computational cost of the original method (<5 minutes on a GPU). We validate our tool on four different modalities (in vivo MRI, ex vivo MRI, HiP-CT, and photography) across a total of approximately 4,000 brain scans. Our results demonstrate that the accelerated approach achieves comparable accuracy to the original method in terms of Dice scores, while reducing runtime by over an order of magnitude. This work enables high-resolution anatomical analysis at unprecedented scale and flexibility, providing a practical solution for large neuroimaging studies. Our tool is publicly available in FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation).

Keywords: Brain MRI segmentation; Domain-agnostic neuroimaging; Histological atlas.

Figure 1:

(a) Coronal and sagittal slices of an ex vivo scan at 0.2 mm resolution (Edlow et al., 2019). (b) Matching cartoon after registration with FireANTs and composite similarity metric. (c) Slices of a 1 mm isotropic, skull-stripped, T1-weighted scan from the MIRIAD dataset (Malone et al., 2013). (d) Registered cartoon. Note the differences in contrast and resolutions of the cartoons (c,d), which attempt to match the scans they seek to segment.

Figure 2:

Axial (top row), coronal (middle), and sagittal slices (bottom) of the publicly available ex vivo scan from Edlow et al., 2019, along with manual delineations (only right hemisphere available) and the automated segmentation with NextBrain. We note that the input was resampled from 0.1 mm to 0.2 mm resolution, which is the voxel size of the manual segmentation, and also the native resolution of NextBrain. We also note that the manual delineation is less granular than the automated segmentation (98 ROIs vs 264).

Figure 3:

Five example subjects from the ex-vivo MRI data set. Coronal view of the MRI data (top) and the corresponding automated segmentation using NextBrain (bottom).

Figure 4:

Segmentation of sample subject (368981431172) from OpenBHB dataset. (a) Axial slice of input. (b) Reference segmentation with supervised U-Net for 1 mm T1 scans. (c) Segmentation using NextBrain, with ROI boundaries highlighted in red. (e-f) Coronal slice of the same case.

Figure 5:

Slices of map of Spearman correlation for ROI volume vs age using OpenBHB dataset, restricted to subjects aged 35 years and over ($n=431$ subjects, mean age: 57.9 years); we chose this age range as 35 is approximately the age when age-related atrophy begins.

Figure 6:

Scatter plot of ICC vs average ROI volume in test-retest experiment on MIRIAD dataset.

Figure 7:

Coronal slices of the five HiP-CT scans (top) and their corresponding automated segmentations using NextBrain (bottom).

Figure 8:

Coronal (left), axial (middle), and sagittal slices (left) of the Visible Human 2.0 dataset, along with automated segmentation computed with NextBrain.