LUMEN-A Deep Learning Pipeline for Analysis of the 3D Morphology of the Cerebral Lenticulostriate Arteries from Time-of-Flight 7T MRI.
Authors
Affiliations (8)
Affiliations (8)
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom. Electronic address: [email protected].
- Genomics Research Centre, Human Technopole, Milan, Italy; Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom; Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom; Department of Neurology, First Affiliated Hospital of Anhui Medical University, Anhui, China.
- Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany.
- Atkinson Morley Regional Neuroscience Centre, St George's University Hospitals NHS Foundation Trust, London, United Kingdom.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
Abstract
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.