Topology-Preserving Polygon Augmentation for Segmentation in Structured Visual Domains

Sudip Laudari; Sang Hun Baek

cs.CVMar 16, 2026

Topology-Preserving Polygon Augmentation for Segmentation in Structured Visual Domains

Authors: Sudip Laudari, Sang Hun Baek

Organizations: Independent Researcher

Abstract

Geometric data augmentation is widely used in segmentation workflows, but polygon annotations are often assumed to remain valid after transformation. This assumption can fail in structured domains such as architectural floorplan analysis, where a region may contain an interior void encoded as part of a single ordered polygon chain. Cropping or clipping can remove bridge vertices in this chain, causing one semantic region to split into disconnected components. We propose a lightweight topology-preserving augmentation strategy that repairs missing adjacency relations in index space while preserving the original vertex order. The method adds minimal overhead and can be integrated into existing preprocessing workflows. Experiments show that the proposed approach achieves near-perfect Cyclic Adjacency Preservation (CAP) across common geometric transformations and improves annotation consistency in polygon-based segmentation.

Figures & tables

Figure 1: Failure of ring-type polygon annotations after geometric augmentation. The ground-truth region is encoded as a single cyclic polygon chain in which bridge edges connect the outer and inner boundaries. After augmentation, these bridge connections can be disrupted, causing the outer or inner region to be treated as a separate polygon.

Figure 2: Overview of the proposed topology-preserving augmentation framework. An input ring region is represented as a single ordered polygon chain $P=(p_{1},\dots,p_{n})$ spanning the outer and inner boundaries. The polygon is rasterized, transformed in mask space as $M^{\prime}=T(M)$ , and its surviving vertex indices are projected onto the augmented boundary. Missing index gaps are then repaired in index space, producing $\hat{P}$ while retaining the intended outer–inner boundary relationship.

Figure 3: Vertex projection after mask-based augmentation. The upper panel shows the transformed indexed polygon, where original vertices are mapped to the augmented boundary while retaining their indices. The lower panel shows the reconstructed polygon after invalid or clipped vertices are removed. This preserves index consistency during augmentation.

Aug.	Parameter	Range
Rotation	Angle	$[-30^{\circ},30^{\circ}]$
Scaling	Scale	$[0.7,1.3]$
Cropping	Crop scale	$[0.6,1.0]$
Rot. + Crop.	Angle / scale	$[-30^{\circ},30^{\circ}]$ , $[0.6,1.0]$
Translation	Shift ratio	$[-0.1,0.1]$ in $x,y$
Flipping	H / V flip	Probability $=0.5$

Table 1: Geometric augmentation parameters used in this study.

Method	CAP ( $\uparrow$ )
YOLO Aug.	0.3278
Roboflow	0.5497
Ours	0.9774

Table 2: CAP results for rotation and common geometric augmentations. Higher values indicate better preservation of cyclic polygon topology.

Figure 4: Illustration of failure cases under Roboflow augmentation. The left column shows the ground-truth ring annotations, with bridge regions highlighted in yellow. The middle and right columns show augmented outputs in which the bridge connection between the outer and inner boundaries is lost, causing the original ring annotation to split into separate outer and inner polygon components.

Figure 5: Visualization of failure cases under YOLO training augmentation with default settings. The upper row shows the ground-truth ring annotations, with bridge regions highlighted in yellow, while the lower row shows the corresponding augmented outputs. After augmentation, the inner boundary is not preserved and the ring annotation is converted into a single outer polygon with dense boundary vertices. This changes the original sparse vertex ordering and removes the intended outer–inner boundary structure.

Figure 6: Illustration of results produced by the proposed method. The upper row shows the ground-truth ring annotations, while the lower row shows the corresponding augmented outputs. The bridge regions remain preserved after augmentation, and the outer and inner boundaries continue to form a single connected ring structure.

Model	Augmentation	mIoU (%) $\uparrow$
YOLOv11-Seg	Standard	88.4
YOLOv11-Seg	Ours	90.8
Mask R-CNN	Standard	85.2
Mask R-CNN	Ours	87.0

Table 3: Segmentation performance under standard augmentation and the proposed topology-preserving augmentation. mIoU is reported in percentage points; higher is better.

Data Augmentation

Data augmentation expands or transforms training examples to improve learning, data efficiency, and robustness to distribution shifts.

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Topology-Preserving Image Segmentation Thin-Structure Segmentation Data Augmentation

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Accurate segmentation of thin, tubular structures (e.g., blood vessels) is challenging for deep neural networks. These networks classify individual pixels, and even minor misclassifications can break the thin connections within these structures. Existing methods that focus on improving topology accuracy, such as topology loss functions, require very precise training labels with the correct topology, which is difficult to obtain. Low image resolution and contrast can further complicate the annotation by causing these structures to appear disconnected. We present CoLeTra, a data augmentation method that improves topology accuracy, even on datasets with topologically inaccurate training labels. CoLeTra achieves this by artificially breaking the structures in the images while maintaining the original labels, incorporating to the models the prior knowledge that structures can be continuous even if they appear disconnected. Our extensive experiments, involving different architectures, loss functions, and datasets, demonstrate that CoLeTra leads to segmentations topologically more accurate while often improving the Dice coefficient and clDice. We also release a dataset specifically suited for image segmentation methods with a focus on topology accuracy. CoLetra's code can be found at https://jmlipman.github.io/CoLeTra.

Abstract

Data Augmentation

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