Physics and Morphology Constrained Quantitative Susceptibility Based Segmentation of Cerebral Veins
- 1Department of Medicine, Faculty of Medicine, Universite de Montreal, Montreal, QC, Canada; Department of Physics, Concordia University, Montreal, QC, Canada
Abstract
Purpose: Quantitative susceptibility mapping (QSM) provides venous contrast through the paramagnetic susceptibility of deoxyhemoglobin and can be used to estimate oxygen extraction fraction (OEF), a marker of cerebral metabolism. However, cerebral vein segmentation remains challenging due to artifacts, variability across QSM reconstruction methods, and limited sensitivity of conventional vessel-filtering approaches to small cortical veins. This study proposes a physics- and morphology-constrained deep learning framework for cerebral vein segmentation on QSM. Methods: Thirty subjects were manually segmented and used to train an attention-gated UNet. In addition to supervised Dice and cross-entropy losses, the network incorporated two selfsupervised constraints. A physics-informed loss enforced consistency between the measured local field and a field simulated from predicted veins using dipole convolution. A Frangi vesselness loss encouraged anatomically plausible tubular structures. Segmentation performance was evaluated on an independent multi-center cohort and compared with a prior vessel-filtering method and a supervised U-Net baseline. Physiological relevance was assessed by comparing QSM-derived venous OEF with calibrated fMRI-derived OEF. Results: The proposed model achieved superior segmentation performance (centreline Dice = 0.70 {+/-} 0.08) compared with MSVF (0.44 {+/-} 0.09) and the U-Net baseline (0.57 {+/-} 0.14). Improvements were most pronounced in smaller veins, resulting in greater vessel continuity. Venous OEF derived from the proposed framework demonstrated stronger agreement with calibrated fMRI-derived OEF than the previous approach. Conclusion: Combining supervised learning with physics- and morphology-based constraints improves vein segmentation and enhances the sensitivity of QSM-derived OEF measurements. Code and model weights are publicly available at https://github.com/QuantitativePhysiologyImagingLab/VeinSeg
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BibTeX
@misc{alkhoury2026physics,
title = {{Physics and Morphology Constrained Quantitative Susceptibility Based Segmentation of Cerebral Veins}},
author = {Y. C. Al-Khoury and A. Rezaei and V. Raspa and N. Slanina and H. Gresseau and Y. Xiao and B. P. Thomas and C. J. Gauthier},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.24.754280},
url = {https://www.biorxiv.org/content/10.64898/2026.09.24.754280v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Physics and Morphology Constrained Quantitative Susceptibility Based Segmentation of Cerebral Veins
AU - Y. C. Al-Khoury
AU - A. Rezaei
AU - V. Raspa
AU - N. Slanina
AU - H. Gresseau
AU - Y. Xiao
AU - B. P. Thomas
AU - C. J. Gauthier
PY - 2026
DA - 2026/09/24
PB - bioRxiv
DO - 10.64898/2026.09.24.754280
UR - https://www.biorxiv.org/content/10.64898/2026.09.24.754280v1
AB - Purpose: Quantitative susceptibility mapping (QSM) provides venous contrast through the paramagnetic susceptibility of deoxyhemoglobin and can be used to estimate oxygen extraction fraction (OEF), a marker of cerebral metabolism. However, cerebral vein segmentation remains challenging due to artifacts, variability across QSM reconstruction methods, and limited sensitivity of conventional vessel-filtering approaches to small cortical veins. This study proposes a physics- and morphology-constrained deep learning framework for cerebral vein segmentation on QSM. Methods: Thirty subjects were manually segmented and used to train an attention-gated UNet. In addition to supervised Dice and cross-entropy losses, the network incorporated two selfsupervised constraints. A physics-informed loss enforced consistency between the measured local field and a field simulated from predicted veins using dipole convolution. A Frangi vesselness loss encouraged anatomically plausible tubular structures. Segmentation performance was evaluated on an independent multi-center cohort and compared with a prior vessel-filtering method and a supervised U-Net baseline. Physiological relevance was assessed by comparing QSM-derived venous OEF with calibrated fMRI-derived OEF. Results: The proposed model achieved superior segmentation performance (centreline Dice = 0.70 {+/-} 0.08) compared with MSVF (0.44 {+/-} 0.09) and the U-Net baseline (0.57 {+/-} 0.14). Improvements were most pronounced in smaller veins, resulting in greater vessel continuity. Venous OEF derived from the proposed framework demonstrated stronger agreement with calibrated fMRI-derived OEF than the previous approach. Conclusion: Combining supervised learning with physics- and morphology-based constraints improves vein segmentation and enhances the sensitivity of QSM-derived OEF measurements. Code and model weights are publicly available at https://github.com/QuantitativePhysiologyImagingLab/VeinSeg
ER -