# VSMOD: A Vessel Segmentation and MODelization plugin for 3D Slicer

Morgane des Ligneris, Gabriel Husak, Azéline Aillet, Guillaume Dollé, Carole Frindel, Odyssée Merveille

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Morgane des Ligneris, Gabriel Husak, Azéline Aillet, Guillaume Dollé, Carole Frindel, et al. VSMOD: A Vessel Segmentation and MODelization plugin for 3D Slicer. Journal of Open Source Software, 2025, 10 (114), pp.8563. 10.21105/joss.08563. hal-05312036

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# VSMOD: A Vessel Segmentation and MODelization plugin for 3D Slicer

Morgane des Ligneris¹, Gabriel Husak¹,², Azéline Aillet², Guillaume Dollé³, Carole Frindel¹,⁴, and Odyssée Merveille¹¶

1 Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France 2 EPITA Research Laboratory (LRE), Le Kremlin-Bicêtre, France 3 Université de Reims Champagne-Ardenne, CNRS, LMR UMR 9008, Reims, France 4 IUF, Institut Universitaire de France, Paris, France ¶ Corresponding author

DOI: 10.21105/joss.08563

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Submitted: 17 April 2025

Published: 10 October 2025

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## Summary

The volumetric annotation of vessels in medical images is a challenging and time-consuming task that typically requires extensive expert manual work. VSMOD is a free, user-friendly plugin for 3D Slicer (Fedorov et al., 2012) designed to simplify and streamline the vascular annotation process. VSMOD offers a semi-automatic two-step segmentation approach that combines RANSAC-based centerline detection (Yureidini et al., 2012) and a region-growing approach with automatic seed selection to accelerate vessel annotation. Users can interactively generate vessel centerlines by placing two points – one marking the vessel’s starting location, and the other indicating its initial direction, thereby defining a branch. Additional branches are created by tracking a new branch near existing ones. From these centerlines, a vascular tree graph is automatically constructed and can be exported as a NetworkX graph (Hagberg et al., 2008), facilitating efficient data storage and external manipulation. Finally, a region-growing segmentation is applied using seeds automatically derived from the centerlines.

Initially designed for pulmonary artery annotation, VSMOD’s methodology is adaptable to various vascular structures across different organs (e.g., brain, liver). This plugin significantly reduces the time required to obtain complete volumetric vessel segmentation. Moreover, it generates fully connected vascular trees with precise topology, which is nearly impossible to achieve with conventional annotation tools.

VSMOD enables users to create accurate and topologically consistent vascular network annotations, facilitating large-scale supervised machine-learning dataset generation for vascular segmentation. Future developments will focus on improving the segmentation step using deep learning-based approaches and further assessing the framework’s generalizability across diverse vascular segmentation tasks.

## Statement of need

Accurate annotation of vascular networks in medical imaging remains a critical challenge and is essential to develop accurate deep learning-based segmentation models. Current segmentation models still struggle with preserving the connectivity of complex vascular networks (Carneiro-Esteves et al., 2025; Keshwani et al., 2020; Rougé et al., 2024), which is crucial for downstream tasks such as flow dynamic simulations. Improving the quality and precision of annotated datasets is crucial for training more effective models. However, traditional manual annotation methods are extremely time-consuming and subject to inter-observer variability, creating important concept shifts in the annotation, ultimately leading to lower model performance (Rougé et al., 2025).
Existing vessel annotation tools have notable limitations. Traditional paint and brush tools in image analysis software such as 3D Slicer<fnref n="1" /> (Pinter et al., 2019) or ImageJ<fnref n="2" /> required slice-by-slice pixel-level annotation, making the process extremely laborious and often resulting in disconnected segmentations. Tubular shape prior tools have been proposed, such as the Draw Tube tool from the SlicerSegmentEditorExtraEffects extension<fnref n="3" />. However, this method requires manually drawing each vessel separately, and its fixed tubular shape is poorly suited for real vessels, which exhibit tortuosity and variable radii. Additionally, constructing a fully connected vascular tree remains particularly challenging.

Recently, Lamy et al. proposed the RVXLiverSegmentation plugin (Lamy et al., 2022) for 3D Slicer to segment vascular networks of the liver. This tool enables fast and accurate vascular segmentation by requiring users to place predetermined points at branching nodes. However, it is designed specifically for the vasculature of the liver and is not applicable to other organs. VSMOD is designed to overcome these limitations by providing a user-friendly plugin for efficiently generating accurate and topologically correct vascular segmentations.

## Overview of VSMOD

VSMOD integrates two complementary modules (cf. Fig. 1). First, the **centerline module** uses a RANSAC centerline-tracking algorithm to create a graph model of the vascular network, ensuring that parent-child relationships between vessels are preserved, and providing precise geometry and radius estimation. Then, the **segmentation module** automatically generates seeds to initialize a region-growing segmentation, yielding a completely connected vascular segmentation.

<figure><img src="image_2.png" /><figcaption>**Figure 1:** Example of the annotation process on pulmonary arteries. From left to right: a) Centerlines detected using the RANSAC-based algorithm. b) 3D visualization of automatically placed seeds for region-growing. c) The final vessel segmentation.</figcaption></figure>

<footnote n="1">¹https://www.slicer.org/</footnote>

<footnote n="2">²https://imagej.net/ij/</footnote>

<footnote n="3">³https://github.com/lassoan/SlicerSegmentEditorExtraEffects</footnote>
## Centerline Module

<figure><img src="image_3.png" /><figcaption>**Figure 2:** Example of the centerline module interface. Green dots represent user-selected points, while blue and pink lines indicate centerlines detected by the RANSAC algorithm. The bottom-left panel displays the vascular graph, where the b1 branch splits into b2 and b3.</figcaption></figure>

The first module of VSMOD extracts vessel centerlines (cf. Fig. 2). Users begin by placing starting and directional points to define the initial vessel path. The RANSAC algorithm (Yureidini et al., 2012) then tracks the vessel centerline automatically. To annotate additional vessels, users select two new points near an existing centerline, and the algorithm connects the new branch to the previously identified centerlines. An estimate of the first vessel diameter is the only required parameter when launching the algorithm for the first time.

Additionally, this module enables users to export and load a vascular network as a NetworkX graph (Hagberg et al., 2008) in JSON format. This feature allows for pausing and resuming the annotation process while preserving the vessel hierarchy and structural relationships for further computational analysis.

## Segmentation Module

<figure><img src="image_4.png" /><figcaption>**Figure 3:** Example of the segmentation module interface with automatically generated seed points on a patient's axial view. The automatic seeds are displayed in different colors inside the vessels (pink, purple and green) and surrounded by the yellow border to constrain the region-growing process.</figcaption></figure>
The second module of VSMOD transforms the extracted centerline data into a volumetric vessel segmentation (cf. Fig. 3). This process leverages the “Grow from seeds” region growing segmentation tool from 3D Slicer (Zhu et al., 2014). Seed points are automatically generated along the detected centerlines, with seed sizes determined by radius estimations from the centerline module. Background seeds create boundaries to constrain the region-growing process within vascular structures.

# Results

## Reduction of the annotation time

In this section, we compare the time to annotate a complete pulmonary vascular network from a computed tomography pulmonary angiography (CTPA).

Ten CTPA images were extensively annotated with the VSMOD plugin by an expert who reported an average time of 3 hours and 35 minutes per image to annotate the complete vascular network (269 vessels on average per image).

Most of this time was spent in the centerline module, where users manually place vessel start and directional points. Additional time was dedicated to refining seed placement around embolized regions, which required extra attention due to their complexity.

Extensive manual annotation of the vascular network using traditional tools was impractical due to the significant time required. Instead, two experts annotated several individual vessels representative of the network's complexity using the available tools in 3D Slicer. The annotation process took an average of four minutes and 46 sec per vessel, which would equate to approximately 21 hours 22 minutes per image for a complete vascular network. VSMOD reduces annotation time by 80% (cf. Table 1).

Beyond efficiency gains, VSMOD offers additional advantages by automatically generating a hierarchical vessel tree, providing centerline data, radius estimations, and graph connectivity information – critical components for downstream vascular analysis.

<table><caption>**Table 1:** Comparison of average segmentation times per patient calculated on data with an average of 269 vessels</caption><thead><tr><td>Annotation method</td><td>Time per vessel</td><td>Total time per patient</td></tr></thead><tbody><tr><td>Manual</td><td>4m46s</td><td>21h22m52s</td></tr><tr><td>VSMOD</td><td>48s</td><td>3h35m12s</td></tr></tbody></table>

## Plugin usage on different vascular networks

VSMOD is not restricted to the vascular network of a specific organ. Its algorithms are generic and rely solely on the assumption of vessel geometry — tube-like structures with curvature. Extensive testing was conducted on the pulmonary vascular network, as discussed in the previous section. Additionally, the plugin was tested on the brain vascular network using magnetic resonance angiography (MRA). Despite the significant geometric differences between pulmonary and cerebral vessels, VSMOD was also able to segment these vessels, as illustrated in Fig. 4.
<figure><img src="image_5.png" /><figcaption>**Figure 4:** Example of the annotation process on brain vessels. From left to right: a) Centerlines detected using the RANSAC-based algorithm. b) 3D visualization of automatically placed seeds for region-growing. c) The final vessel segmentation.</figcaption></figure>

In conclusion, VSMOD provides a user-friendly framework for generating vascular network segmentation annotations. By automating the most labor-intensive aspects of vascular segmentation, this module significantly reduces manual effort while still allowing users to fine-tune the results as needed.

Future work will focus on optimizing RANSAC parameters by introducing anatomy-specific default settings for different types of vessels. This would minimize the need for manual fine-tuning, improving usability and adaptability. Additionally, integrating deep-learning models could further enhance segmentation accuracy and reduce manual intervention, particularly in the segmentation module.

## Acknowledgements

The vascular graph modeling is inspired by the hierarchical vessel organization proposed in *RVXLiverSegmentation* (Lamy et al., 2022).

This work was founded by the French ANR through the PERSEVERE and PreSPIN projects (ANR-22-CE45-0018, ANR-20-CE45-0011). This work was also performed within the framework of the LABEX PRIMES (ANR-11-LABX-0063).

## References

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Hagberg, A. A., Schult, D. A., & Swart, P. J. (2008). Exploring network structure, dynamics, and function using NetworkX. *Scipy*. https://doi.org/10.25080/TCWV9851

Keshwani, D., Kitamura, Y., Ihara, S., Iizuka, S., & Simo-Serra, E. (2020). TopNet: Topology preserving metric learning for vessel tree reconstruction and labelling. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (Eds.), *Medical image computing and computer assisted intervention – MICCAI 2020* (pp. 14–23). Springer International Publishing. https://doi.org/10.1007/978-3-030-59725-2_2

Lamy, J., Pelletier, T., Lienemann, G., Magnin, B., Kerautret, B., Passat, N., Finet, J., &
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