LUMEN–A deep learning pipeline for analysis of the 3D morphology of the cerebral lenticulostriate arteries from time-of-flight 7T MRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 318; p. 121377
• Main Authors: Li, Rui, Chatterjee, Soumick, Jiaerken, Yeerfan, Zhou, Xia, Radhakrishna, Chethan, Benjamin, Philip, Nannoni, Stefania, Tozer, Daniel J., Markus, Hugh S., Rodgers, Christopher T.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2025; Elsevier Limited
• Subjects: Algorithms; Angiography; Arteries; Automation; Basal ganglia; Cardiovascular diseases; Cerebral Small Vessel Disease; Contrast agents; Contrast-Enhanced 7T-TOF-MRA; Deep Learning; Disease; Image processing; Lenticulostriate Arteries; Morphology; MRI; Radiology/Diagnostic Imaging; Risk factors; Segmentation; Stroke; Vascular diseases; Veins & arteries; nnU-Net; Cerebral Small Vessel Disease; Contrast-Enhanced 7T-TOF-MRA; CSVD; MSFDF; MRI; DS6; DL; MCA; CamSVD; ROI; Deep Learning; TOF-MRA; MIP; bAHD; Lenticulostriate Arteries; LSA; LUMEN; ACA; Multi-Scale Frangi Diffusive Filter; Name of the self-configuring segmentation toolbox; Middle cerebral artery; Anterior cerebral artery; Balanced average Hausdorff distance; Time-of-Flight magnetic resonance angiography; Name of the Cambridge 7T Cerebral Small Vessel Disease study cohort; Lenticulostriate artery; Maximum intensity projection; Name of deep-learning vessel segmentation model; Lenticulostriate artery Ultra-high-field Morphology Extraction and quantificatioN (i.e. this toolbox); Region-of-interest
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2025.121377

•3D morphology of lenticulostriate arteries may indicate early CSVD pathology.•Present LUMEN pipeline for analysing 3D morphology of LSAs in CSVD from 7T TOF-MRA.•Fine-tuned DL model achieved a test Dice score of 0.814±0.029 in LSA segmentation.•Showed limitations of 2D MIP analysis of LSA morphology compared to 3D analysis. The lenticulostriate arteries (LSAs) supply critical subcortical brain structures and are affected in cerebral small vessel disease (CSVD). Changes in their morphology are linked to cardiovascular risk factors and may indicate early pathology. 7T Time-of-Flight MR angiography (TOF-MRA) enables clear LSA visualisation. We aimed to develop a semi-automated pipeline for quantifying 3D LSA morphology from 7T TOF-MRA in CSVD patients. We used data from a local 7T CSVD study to create a pipeline, LUMEN, comprising two stages: vessel segmentation and LSA quantification. For segmentation, we fine-tuned a deep learning model, DS6, and compared it against nnU-Net and a Frangi-filter pipeline, MSFDF. For quantification, centrelines of LSAs within basal ganglia were extracted to compute branch counts, length, tortuosity, and maximum curvature. This pipeline was applied to 69 subjects, with results compared to traditional analysis measuring LSA morphology on 2D coronal maximum intensity projection (MIP) images. For vessel segmentation, fine-tuned DS6 achieved the highest test Dice score (0.814±0.029) and sensitivity, whereas nnU-Net achieved the best balanced average Hausdorff distance and precision. Visual inspection confirmed that DS6 was most sensitive in detecting LSAs with weak signals. Across 69 subjects, the pipeline with DS6 identified 23.5 ± 8.5 LSA branches. Branch length inside the basal ganglia was 26.4 ± 3.5 mm, and tortuosity was 1.5 ± 0.1. Extracted LSA metrics from 2D MIP analysis and our 3D analysis showed fair-to-moderate correlations. Outliers highlighted the added value of 3D analysis. This open-source deep-learning-based pipeline offers a validated tool quantifying 3D LSA morphology in CSVD patients from 7T-TOF-MRA for clinical research. [Display omitted]