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Cerebral Small Vessel Disease / Cerebral Small Vessel Diseases / Alzheimer Disease · Journal article
Magnetic Resonance Imaging · July 22, 2026
Encouraging direction, but not yet definitive.
DORES, a 3D deep learning algorithm for automated ePVS segmentation, achieved moderate Dice scores (0.61–0.72) and strong within-cohort correlations on older adult MRI scans, but demonstrated decline in external validation and scanner-dependent measurement inconsistency. The tool shows promise for cohort-level use but requires further development to enable cross-site applicability.
Single-centre prospective algorithm development study with internal and external validation. VMAP: older adults (71 ± 8 years, 54% male for visual rating subset; 73 ± 9 years, 51% male for manual tracing subset) with 3 T brain MRI. ADNI3: older adults (73 ± 9 years, 67% female) with 3 T brain MRI.. Intervention: DORES automated ePVS segmentation algorithm using T1-weighted and FLAIR MRI. Compared with: Three other segmentation tools; manual whole-brain ePVS tracing (reference standard). n = 35. Vanderbilt Memory and Aging Project (VMAP) at single centre; external validation used Alzheimer's Disease Neuroimaging Initiative 3 (ADNI3) multi-site data..
DORES Dice score 0.61 ± 0.16 (white matter) and 0.72 ± 0.08 (basal ganglia) in VMAP internal validation (n=35) Strong correlations and agreement for ePVS count and volume within VMAP cohort Performance modestly declined in external ADNI3 validation (n=18)
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DORES offers a feasible automated pipeline for ePVS quantification in research cohorts, enabling large-scale studies of this small-vessel-disease marker. However, scanner effects and modest external validity suggest caution in applying the model across different imaging sites without site-specific recalibration.
A single-centre algorithm development study with internal validation on 35 manual tracings and external validation on 18 scans, showing moderate segmentation accuracy (Dice 0.61–0.72) but acknowledged scanner-dependent variability that limits generalisability.
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DORES offers a feasible automated pipeline for ePVS quantification in research cohorts, enabling large-scale studies of this small-vessel-disease marker. However, scanner effects and modest external validity suggest caution in applying the model across different imaging sites without site-specific recalibration.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Background. Enlarged perivascular spaces (ePVS) are a marker of cerebral small vessel disease, potentially reflecting reduced waste clearance. Because manual quantification is unfeasible in large datasets, we developed and evaluated an automated tool.Methods. Detection Of Regions of Enlarged perivascular Spaces (DORES), a 3D nnU-Net-based deep learning algorithm was developed for ePVS segmentation using T1-weighted and fluid-attenuated inversion recovery magnetic resonance imaging (MRI). DORES was developed in two stages: an initial model trained on 35 manually segmented scans and a final model on 1460 pseudo-labeled sessions from the Vanderbilt Memory and Aging Project (VMAP). A subset of VMAP participants with 3 T brain MRI underwent whole-brain manual ePVS tracing (n = 35, 73 ± 9 years, 51% male) and visual rating (n = 388, 71 ± 8 years, 54% male) by a neuroradiologist. DORES was evaluated and compared against three other segmentation tools using Dice and F1 scores, absolute volume and element differences, correlation, and agreement. External validation used an Alzheimer's Disease Neuroimaging Initiative 3 subset with manual tracings (ADNI3, n = 18, 73 ± 9 years, 67% female).Results. DORES achieved Dice scores of 0.61 ± 0.16 (white matter) and 0.72 ± 0.08 (basal ganglia) in VMAP, with strong correlations and agreement for ePVS count and volume. Performances modestly declined in ADNI3 across algorithms. Scanner-stratified analyses showed stronger correlations for Philips versus Siemens images in the basal ganglia, indicating scanner-dependent differences in measurement consistency.Conclusions. DORES provides a multimodal nnU-Net-based pipeline for ePVS segmentation in older adults. The model demonstrates robust within-cohort performance and reasonable external validity, though scanner-related effects limit application across sites.
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