Technology Category
Computer Vision: SegmentationKnowledge Representation and Reasoning: Diagnosis and Abductive ReasoningConstraint Satisfaction and Optimization: Satisfiability Modulo Theories
A Physics-Informed Loss Function for Boundary-Consistent and Robust Artery Segmentation in DSA Sequences
🤖 AI Summary
Traditional pixel-wise loss functions for cerebral artery segmentation in digital subtraction angiography (DSA) sequences neglect geometric and physical consistency, leading to fragmented boundaries and unstable predictions. To address this, we propose a physics-informed loss (PIL) that, for the first time, incorporates dislocation theory from materials physics into medical image segmentation—modeling elastic interactions along vascular boundaries to physically regularize contour evolution. PIL is architecture-agnostic and seamlessly integrates with mainstream segmentation frameworks—including U-Net, U-Net++, SegFormer, and MedFormer—jointly optimizing pixel-wise overlap and boundary dynamics. Evaluated on the DIAS and DSCA datasets, PIL significantly improves sensitivity, F1 score, and boundary coherence, consistently outperforming cross-entropy, Dice, and active contour losses. The implementation is publicly available.
Technology Category
Application Category
📝 Abstract
Accurate extraction and segmentation of the cerebral arteries from digital subtraction angiography (DSA) sequences is essential for developing reliable clinical management models of complex cerebrovascular diseases. Conventional loss functions often rely solely on pixel-wise overlap, overlooking the geometric and physical consistency of vascular boundaries, which can lead to fragmented or unstable vessel predictions. To overcome this limitation, we propose a novel extit{Physics-Informed Loss} (PIL) that models the interaction between the predicted and ground-truth boundaries as an elastic process inspired by dislocation theory in materials physics. This formulation introduces a physics-based regularization term that enforces smooth contour evolution and structural consistency, allowing the network to better capture fine vascular geometry. The proposed loss is integrated into several segmentation architectures, including U-Net, U-Net++, SegFormer, and MedFormer, and evaluated on two public benchmarks: DIAS and DSCA. Experimental results demonstrate that PIL consistently outperforms conventional loss functions such as Cross-Entropy, Dice, Active Contour, and Surface losses, achieving superior sensitivity, F1 score, and boundary coherence. These findings confirm that the incorporation of physics-based boundary interactions into deep neural networks improves both the precision and robustness of vascular segmentation in dynamic angiographic imaging. The implementation of the proposed method is publicly available at https://github.com/irfantahir301/Physicsis_loss.
Problem
Research questions and friction points this paper is trying to address.
Develops physics-informed loss for robust cerebral artery segmentation in DSA sequences
Addresses fragmented vessel predictions by enforcing geometric boundary consistency
Improves segmentation precision through physics-based vascular boundary regularization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Physics-Informed Loss models boundary interaction elastically
Integrates dislocation theory for smooth contour evolution
Enforces structural consistency in vascular segmentation networks
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2024-06-01arXiv.orgCitations: 0
