Mind the Gap: Mesh-Guided Repair of Broken Vessels

Gniewosz Drwiega; Wojciech Szymanski; Marek Wodzinski

cs.CVSep 24, 2026

Mind the Gap: Mesh-Guided Repair of Broken Vessels

Authors: Gniewosz Drwiega, Wojciech Szymanski, Marek Wodzinski

Organizations: Sano – Centre for Computational Personalised Medicine International Research Foundation, Krakow, Poland · AGH University of Krakow, Krakow, Poland

Abstract

Vessel segmentation is commonly optimized as voxel-wise classification, but small local errors can strongly disrupt vascular connectivity while having little effect on overlap scores. This is particularly problematic for downstream analyses that rely on centerlines, branches, connected components, or graph structure. We propose a mesh-guided post-processing framework for repairing broken vessel segmentations produced by nnU-Net. For each predicted binary mask, a deformable template mesh is fitted to the mask surface in physical space and used as a case-specific geometric scaffold. The fitted mesh is not voxelized as the final segmentation; instead, it guides conservative reconnection of disconnected components by proposing or validating thin bridge candidates under foreground-growth constraints. We evaluated this approach in three vascular anatomies using AortaSeg24 and SEGA for the aorta, TopCoW for the Circle of Willis, and PARSE for the pulmonary arteries. Performance is measured using Dice, connected-component Dice (ccDice), and the Betti-0 number. Across these datasets, repair substantially improved connectivity while preserving overlap: Dice remained nearly unchanged, whereas ccDice increased from 0.596 to 0.992 for aorta, from 0.722 to 0.835 for TopCoW, and from 0.028 to 0.862 for PARSE. The FOMAML meta-initialization further accelerated the fitting per-case, supporting practical mesh-based repair of the vascular topology. These results suggest that explicit mesh representations can provide a useful geometric prior for correcting topological failures in otherwise accurate voxel segmentations.

Figures & tables

Figure 1: Examples of broken-vessel failure modes across vascular anatomies. Top row: reference vessel structures. Bottom row: broken nnU-Net predictions, with missing local connections highlighted. Such small local errors can fragment otherwise accurate masks.

Figure 2: Overview of the proposed mesh-guided nnU-Net repair pipeline. A CT volume is first processed by a CNN segmentation branch based on nnU-Net, producing segmented vessel sections and an initial nnU-Net mask. When the predicted mask is fragmented or topologically inconsistent, its surface is extracted and used as the target for mesh fitting. Then, a fixed template mesh is passed through a GCN deformation branch. The mesh is progressively refined over multiple deformation stages, where each stage predicts bounded vertex offsets and forwards the updated mesh to the next refinement stage. The final overfitted mesh approximates the surface geometry of the nnU-Net prediction while preserving the template mesh connectivity. This fitted mesh is then used as a geometric prior for topology-aware mask repair, producing a repaired nnU-Net mask with improved connectivity.

Dataset	Template	Edge	Lap.	Normal / Area
Aorta Unified	Cylinder, 10k	0.03125	1.25	$2.5{\times}10^{-4}$ / $2.5{\times}10^{-5}$
TopCoW	Torus-like, 10k	0.000625	0.025	$2.5{\times}10^{-4}$ / $2.5{\times}10^{-5}$
PARSE	Sphere, 20k	0.1	5.0	$1.0{\times}10^{-3}$ / $5.0{\times}10^{-4}$

Table 1: Dataset-specific mesh-fitting hyperparameters.

Parameter	Aorta Unified	TopCoW	PARSE
Repair variant	Mesh-graph path	Mesh-graph path	Endpoint + mesh validation
Artifact keep/remove (mm)	57.0 / 57.5	57.0 / 57.5	20.0 / 25.0
Bridge anchor/support	Anchor 3.0 mm	Anchor 3.0 mm	Gap 12.0 mm; support 3.0 mm; fraction 0.50
Tube radius base/min/max (mm)	1.0 / 2.0 / 5.0	0.25 / 0.35 / 1.0	0.4 / 0.7 / 1.8
Adaptive radius percentile/scale	80 / 1.0	80 / 0.7	70 / 0.8
Max added foreground fraction	0.03	0.15	0.06

Table 2: Dataset-specific mask-repair hyperparameters.

Figure 3: Qualitative examples of mesh-guided repair across vascular anatomies. The method reconnects fragmented vessel components using local mesh-guided bridges while preserving the original mask away from the repaired regions. Blue arrows indicate broken vessel regions repaired by the proposed method.

Dataset	Mask	Dice $\uparrow$	ccDice $\uparrow$	$\beta_{0}$ $\downarrow$	FB $\downarrow$
Aorta ( $n=145$ )	Raw	$0.934\pm 0.007$	$0.596\pm 0.045$	$3.35\pm 0.43$	$0.056\pm 0.009$
	Filtered	$\mathbf{0.935\pm 0.007}$	$0.695\pm 0.043$	$2.47\pm 0.30$	$\mathbf{0.044\pm 0.008}$
	Repaired	$0.934\pm 0.007$	$\mathbf{0.992\pm 0.009}$	$\mathbf{1.01\pm 0.02}$	$0.055\pm 0.009$
TopCoW ( $n=125$ )	Raw	$\mathbf{0.870\pm 0.010}$	$0.722\pm 0.043$	$2.58\pm 0.20$	$\mathbf{0.040\pm 0.006}$
	Filtered	$\mathbf{0.870\pm 0.010}$	$0.723\pm 0.043$	$2.58\pm 0.20$	$\mathbf{0.040\pm 0.006}$
	Repaired	$0.867\pm 0.010$	$\mathbf{0.835\pm 0.034}$	$\mathbf{1.02\pm 0.02}$	$0.063\pm 0.007$

Table 3: Segmentation and connectivity metrics before and after repair. Values are reported as mean

\pm

95% confidence interval. Best values within each dataset are shown in bold. FB denotes false-branch fraction.

Figure 4: Quantitative summary of repair effects and mesh-fitting convergence. Mesh-guided repair improves connectivity-oriented metrics while preserving Dice, and meta-initialization accelerates per-case mesh fitting.

Figure 5: Effect of FOMAML meta-initialization on per-case mesh-fitting convergence for 25 held-out TopCoW cases. Lines show mean Chamfer loss across cases, and shaded regions indicate

\pm

SEM. The black horizontal line indicates a matched Chamfer-distance level, allowing comparison of the number of optimization steps required by FOMAML and standard initialization to reach the same reconstruction error.

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Blood vessel segmentation and -tracing are essential tasks in many medical imaging applications. Although numerous methods exist, the prevailing segment-then-fix paradigm is fundamentally limited regarding its suitability for modeling the task of complete and topologically accurate vascular network reconstruction. Here, we propose an approach to extract topologically more accurate vascular graphs from 3D image data, building upon highly successful ideas from the related biomedical tasks of cell segmentation and -tracking. Our approach first predicts voxel-wise vessel direction vectors joint with standard vessel segmentation masks. Second, to extract the vascular graph from these predictions, we introduce a direction-vector-guided extension of the TEASAR algorithm. Our approach achieves state-of-the-art performance on three benchmark datasets, spanning both synthetic and real imagery. We further demonstrate the applicability of our approach to challenging 3D micro-CT scans of rat heart vasculature. Finally, we propose meaningful and interpretable measures of topological error, namely false splits and false merges for graphs. Overall, our approach substantially improves the topological accuracy of reconstructed vascular graphs, being able to separate closely apposed vessel segments and handle multiple vascular trees within a single volume.

Abstract

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