# The 3D Slicer RVXLiverSegmentation plug-in for interactive liver anatomy reconstruction from medical images

Jonas Lamy, Thibault Pelletier, Guillaume Lienemann, Benoît Magnin, Bertrand Kerautret, Nicolas Passat, Julien Finet, Antoine Vacavant

<figure><img src="image_1.png" /></figure>

## To cite this version:

Jonas Lamy, Thibault Pelletier, Guillaume Lienemann, Benoît Magnin, Bertrand Kerautret, et al. The 3D Slicer RVXLiverSegmentation plug-in for interactive liver anatomy reconstruction from medical images. Journal of Open Source Software, 2022, 7 (73), pp.3920. 10.21105/joss.03920. hal-03649993

HAL Id: hal-03649993

https://hal.science/hal-03649993

Submitted on 3 May 2022

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
The 3D Slicer RVXLiverSegmentation plug-in for
interactive liver anatomy reconstruction from medical
images

Jonas Lamy*¹, Thibault Pelletier|², Guillaume Lienemann|³, Benoît Magnin³, Bertrand Kerautret¹, Nicolas Passat⁴, Julien Finet², and Antoine Vacavant³¶

1 Université Lyon 2, LIRIS (UMR 5205) Lyon, France 2 Kitware SAS, Villeurbanne, France 3

Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand,

France 4 Université de Reims Champagne Ardenne, CReSTIC, EA 3804, 51100 Reims, France ¶

Corresponding author

DOI: 10.xxxxxx/draft

Software

■ Review

<figure><img src="image_3.png" /></figure>

■ Repository

<figure><img src="image_4.png" /></figure>

■ Archive

<figure><img src="image_5.png" /></figure>

Editor: Kevin M. Moerman

<figure><img src="image_6.png" /></figure>

Reviewers:

■ @lassoan

■ @deepakri201

Submitted: 05 October 2021

Published: unpublished

License

Authors of papers retain
copyright and release the work
under a Creative Commons
Attribution 4.0 International
License (CC BY 4.0).

11

12

13

14

15

16

17

18

19

20

21

22

Summary

RVXLiverSegmentation is a 3D Slicer (Kikinis et al., 2014, 2022) plug-in aiming at speeding-
up the annotation of liver anatomy from medical images (CT -Computerized Tomography-
scans or MRI -Magnetic Resonance Imaging- for instance). This organ has particular anatomy
and physiology; within its parenchymal volume, the liver receives blood from the portal vein and
hepatic artery (the former one being the most visible in medical images), and delivers filtered
blood through the hepatic veins, toward inferior vena cava. The blood vessels subdivide into the
liver as fine vascular tree structures, which makes the segmentation difficult, especially in the
case of MRI. To facilitate this task, our plug-in is decomposed into modules dedicated to the
segmentation of the liver volume, inner vessels and possible tumors. RVXLiverSegmentation
can be downloaded from (Pelletier et al., 2022) or installed from the 3D Slicer software as
an official module.

Statement of need

Annotation plays a key role in clinical practice and in the creation of reference datasets that are useful to evaluate medical image processing algorithms and to train machine learning based architectures (Park et al., 2020). Since softwares embedded into acquisition machines are not always accurate for this task, researchers and engineers have developed more and more interactive annotation and segmentation tools (Wang et al., 2021). But, despite these developments, annotated datasets are still missing for many clinical applications. The creation of the RVXLiverSegmentation plug-in has been motivated by the dramatic absence of a public dataset providing hepatic MRI data together with their ground-truth segmentations of liver anatomy. By creating our own dataset, we have developed a plug-in specifically dedicated to this task, that can be used publicly by any user for the construction of new datasets related to liver pathologies. Moreover, our plug-in has numerous clinical applications, such as the hepatic volumetry, which is becoming increasingly important in view of the growing number of chronic hepatopathies requiring transplants or hepatectomies (Lim et al., 2014).

24

25

26

27

28

29

30

31

32

33

34

35

36

<footnote>*co-first author</footnote>

<footnote>†co-first author</footnote>

<footnote>‡co-first author</footnote>
# Overview of RVXLiverSegmentation

For research purpose needing annotation of liver anatomy from medical images, the RVXLiver
Segmentation provides 7 main tabs:

* loading and managing medical imaging data;

* liver segmentation;

* annotation of portal veins and segmentation;

* editing portal veins segmentation;

* annotation of inferior vena cava and segmentation;

* editing inferior vena cava segmentation;

* tumor segmentation.

Once the medical image data is loaded into the 3D Slicer interface, the liver can be segmented with the associated tab, either by using interactive tools (such as region growing approaches) or by an automatic deep learning based algorithm (for CT scans only), as exposed in Figure 1. Then, the reconstructions of hepatic vessels (portal vein and inferior vena cava) are based on tree structures interactively built by the user, who places the nodes of important branches and bifurcations (with specific anatomical nomenclature) into the scene of the medical image to be processed. After this step, a VMTK (Vascular Modeling Tool Kit) (Antiga et al., 2008) module segments the vessels by using those graphs as initialization patterns (see Figure 2); also, the user can verify and edit this segmentation. The last tab allows the user to segment interactively possible tumoral tissues with dedicated tools.

This tab also permits to export the complete scene, comprising:

* segmentation label maps (liver, inferior vena cava, portal vein, tumors);

* portal vein and inferior vena cava intersection positions (fiducial CSV and adjacency matrix);

* the complete scene as a MRB file.

<figure><img src="image_7.png" /><figcaption>Figure 1: Liver segmentation tab.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 2: Tab for portal vein annotation and segmentation.</figcaption></figure>

## Preliminary results obtained with the plug-in

A first version of the RVXLiverSegmentation has been employed for segmenting livers from dynamic-contrast enhanced MRI data in order to test and evaluate a combined registration-segmentation algorithm, described in (Debroux et al., 2020). We have also compared the time of segmentations obtained by RVXLiverSegmentation and by the embedded image processing tool General Electric AW solution (Server 3.2). Our first results (Lamy et al., 2020) have shown a significant speed-up in the segmentation of liver volume and inner vessels. The following averaged times were measured with a first cohort of 6 "healthy" patients (i.e. not suffering from any hepatic disease) and 4 patients with liver cancer and cirrhosis.

<table><thead><tr><th>Healthy patients</th><th>RVXLiverSegmentation</th><th>General Electric AW</th></tr></thead><tbody><tr><td>Liver</td><td>3 ± 2 mins</td><td>10 ± 5 mins</td></tr><tr><td>Vessels</td><td>5 ± 3 mins</td><td>30 ± 10 mins</td></tr></tbody></table>

<table><thead><tr><th>Cirrhotic patients</th><th>RVXLiverSegmentation</th><th>General Electric AW</th></tr></thead><tbody><tr><td>Liver</td><td>4 ± 2 mins</td><td>15 ± 10 mins</td></tr><tr><td>Vessels</td><td>7 ± 4 mins</td><td>35 ± 15 mins</td></tr></tbody></table>

## Future works

We first would like to integrate advanced deep learning models for liver and hepatic vessels segmentation (Affane et al., 2021) into our RVXLiverSegmentation plug-in, in order to provide automatic reconstructions that can be then edited by the user with the other tools proposed in the plug-in and in 3D Slicer. Another important work concerns the VMTK module, which needs more adaptations for MRI processing. As an example, the Frangi filter is employed as a pre-processing step (as a vascular enhancement algorithm), while other approaches could be opted instead (Lamy et al., 2021). Finally, a more complete evaluation protocol will be conducted with our plug-in, compared to commercial solutions, by taking into account larger patient cohorts.
Acknowledgements

This work was funded by the French Agence Nationale de la Recherche (grant ANR-18-CE45-0018, project R-Vessel-X, http://tgi.ip.uca.fr/r-vessel-x).

References

Affane, A., Kucharski, A., Chapuis, P., Freydier, S., Lebre, M.-A., Vacavant, A., & Fabijańska, A. (2021). Segmentation of liver anatomy by combining 3-D U-Net approaches. *MDPI Journal of Applied Sciences, 11*(11:4895). https://doi.org/10.3390/app11114895

Antiga, L., Piccinelli, M., Botti, L., Ene-lordache, B., Remuzzi, A., & Steinman, D. A. (2008). An image-based modeling framework for patient-specific computational hemodynamics. *MBEC, 46*, 1097-1112. https://doi.org/10.1007/s11517-008-0420-1

Debroux, N., Lienemann, G., Magnin, B., Guyader, C. L., & Vacavant, A. (2020). A time-dependent joint segmentation and registration model: Applications to longitudinal registration of hepatic DCE-MRI sequences. *IEEE IPTA 2020*. https://doi.org/10.1109/ipta50016.2020.9286658

Kikinis, R., Pieper, S. D., & Fillion-Robin, J. P. (2022). 3D Slicer. https://www.slicer.org

Kikinis, R., Pieper, S. D., & Vosburgh, K. (2014). 3D Slicer: A platform for subject-specific image analysis, visualization, and clinical support. In F. A. Jolesz (Ed.), *Intraoperative Imaging Image-Guided Therapy* (Vol. 3(19), pp. 277-289). https://doi.org/10.1007/978-1-4614-7657-3_19

Lamy, J., Merveille, O., Kerautret, B., Passat, N., & Vacavant, A. (2021). Vesselness filters: A survey with benchmarks applied to liver imaging. *IEEE ICPR 2020*. https://doi.org/10.1109/icpr48806.2021.9412362

Lamy, J., Pelletier, T., Lienemann, G., Magnin, B., Kerautret, B., Passat, N., Finet, J., & Vacavant, A. (2020). Preliminary results with a new annotation tool for liver volume and inner vessels from DCE-MRI data. *VPH 2020*.

Lim, M. C., Tan, C. H., Cai, J., Zheng, J., & Kow, A. W. (2014). CT volumetry of the liver: Where does it stand in clinical practice? *Clinical Radiology, 69*(9), 887-895. https://doi.org/10.1016/j.crad.2013.12.021

Park, S., Chu, L. C., Fishman, E. K., Yuille, A. L., Vogelstein, B., Kinzler, K. W., Horton, K. M., Hruban, R. H., Zinreich, E. S., Fouladi, D. F., Shayesteh, S., Graves, J., & Kawamoto, S. (2020). Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation. *Diagnostic and Interventional Imaging, 101*(1), 35-44. https://doi.org/10.1016/j.diii.2019.05.008

Pelletier, T., Finet, J., & Vacavant, A. (2022). RVesselX slicer liver anatomy annotation plugin. In *GitHub repository*. GitHub. https://github.com/R-Vessel-X/SlicerRVXLiverSegmentation

Wang, S., Li, C., Wang, R., Liu, Z., Wang, M., Tan, H., Wu, Y., Liu, X., Sun, H., Yang, R., Liu, X., Chen, J., Zhou, H., Ben Ayed, I., & Zheng, H. (2021). Annotation-efficient deep learning for automatic medical image segmentation. *Nature Communications, 12*(5915). https://doi.org/10.1038/s41467-021-26216-9