Skip to main content
SpringerLink
Log in

Signal Intensity Trajectories Clustering for Liver Vasculature Segmentation and Labeling (LiVaS) on Contrast-Enhanced MR Images: A Feasibility Pilot Study

  • Original Paper
  • Published:
Journal of Imaging Informatics in Medicine Aims and scope Submit manuscript

Abstract

This study aims to develop a semiautomated pipeline and user interface (LiVaS) for rapid segmentation and labeling of MRI liver vasculature and evaluate its time efficiency and accuracy against manual reference standard. Retrospective feasibility pilot study. Liver MR images from different scanners from 36 patients were included, and 4/36 patients were randomly selected for manual segmentation as referenced standard. The liver was segmented in each contrast phase and masks registered to the pre-contrast segmentation. Voxel-wise signal trajectories were clustered using the k-means algorithm. Voxel clusters that best segment the liver vessels were selected and labeled by three independent radiologists and a research scientist using LiVaS. Segmentation times were compared using a paired-sample t-test on log-transformed data. The agreement was analyzed qualitatively and quantitatively using DSC for hepatic and portal vein segmentations. The mean segmentation time among four readers was significantly shorter than manual (3.6 ± 1.4 vs. 70.0 ± 29.2 min; p < 0.001), even when using a higher number of clusters to enhance accuracy. The DSC for portal and hepatic veins reached up to 0.69 and 0.70, respectively. LiVaS segmentations were overall of good quality, with variations in performance related to the presence/severity of liver disease, acquisition timing, and image quality. Our semi-automated pipeline was robust to different MRI vendors in producing segmentation and labeling of liver vasculature in agreement with expert manual annotations, with significantly higher time efficiency. LiVaS could facilitate the creation of large, annotated datasets for training and validation of neural networks for automated MRI liver vascularity segmentation.

Highlights

Key Finding: In this pilot feasibility study, our semiautomated pipeline for segmentation of liver vascularity (LiVaS) on MR images produced segmentations with simultaneous labeling of portal and hepatic veins in good agreement with the manual reference standard but at significantly shorter times (mean LiVaS 3.6 ± 1.4 vs. mean manual 70.0 ± 29.2 min; p < 0.001).

Importance: LiVaS was robust in producing liver MRI vascular segmentations across images from different scanners in agreement with expert manual annotations, with significant ly higher time efficiency, and therefore potential scalability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

Study data can be provided at reasonable request to the corresponding author. LiVaS code including the UI is publicly available at https://zenodo.org/record/7989974.

Abbreviations

AP:

Arterial phase

DSC:

Dice similarity coefficient

HA:

Hepatic arteries

HV:

Hepatic veins

MRI:

Magnetic resonance imaging

PV:

Portal veins

UI:

User interface

References

  1. Gotra A, Sivakumaran L, Chartrand G, Vu KN, Vandenbroucke-Menu F, Kauffmann C, et al. Liver segmentation: indications, techniques and future directions. Insights Imaging. 2017 Aug;8(4):377–92.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Martí-Aguado D, Jiménez-Pastor A, Alberich-Bayarri Á, Rodríguez-Ortega A, Alfaro-Cervello C, Mestre-Alagarda C, et al. Automated Whole-Liver MRI Segmentation to Assess Steatosis and Iron Quantification in Chronic Liver Disease. Radiology. 2022 Jan;302(2):345–54.

    Article  PubMed  Google Scholar 

  3. Zhang QH, Zhao Y, Tian SF, Xie LH, Chen LH, Chen AL, et al. Hepatic fat quantification of magnetic resonance imaging whole-liver segmentation for assessing the severity of nonalcoholic fatty liver disease: comparison with a region of interest sampling method. Quant Imaging Med Surg. 2021 Jul;11(7):2933–42.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Abdalla EK, Denys A, Chevalier P, Nemr RA, Vauthey JN. Total and segmental liver volume variations: Implications for liver surgery. Surgery. 2004 Apr;135(4):404–10.

    Article  PubMed  Google Scholar 

  5. Wang K, Mamidipalli A, Retson T, Bahrami N, Hasenstab K, Blansit K, et al. Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network. Radiol Artif Intell. 2019 Mar;1(2):180022.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Winkel DJ, Weikert TJ, Breit HC, Chabin G, Gibson E, Heye TJ, et al. Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation. Eur J Radiol. 2020 May;126:108918.

    Article  PubMed  Google Scholar 

  7. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. 2012. p. 1097–105.

  8. Goceri E. Automatic labeling of portal and hepatic veins from MR images prior to liver transplantation. Int J Comput Assist Radiol Surg. 2016 Dec;11(12):2153–61.

    Article  PubMed  Google Scholar 

  9. Ciecholewski M, Kassjański M. Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review. Sensors. 2021 Mar 12;21(6):2027.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bauer DF, Russ T, Waldkirch BI, Tönnes C, Segars WP, Schad LR, et al. Generation of annotated multimodal ground truth datasets for abdominal medical image registration. Int J Comput Assist Radiol Surg. 2021 Aug;16(8):1277–85.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Tustison NJ, Avants BB, Cook PA, Yuanjie Zheng, Egan A, Yushkevich PA, et al. N4ITK: Improved N3 Bias Correction. IEEE Trans Med Imaging. 2010 Jun;29(6):1310–20.

  12. Johnson J, Douze M, Jegou H. Billion-Scale Similarity Search with GPUs. IEEE Trans Big Data. 2021 Jul 1;7(3):535–47.

    Article  Google Scholar 

  13. Hunter JD. Matplotlib: A 2D Graphics Environment. Comput Sci Eng. 2007;9(3):90–5.

    Article  Google Scholar 

  14. Rossum G van, Drake FL. The Python language reference. Release 3.0.1 [Repr.]. Hampton, NH: Python Software Foundation; 2010. 109 p. (Python documentation manual / Guido van Rossum; Fred L. Drake [ed.]).

  15. Zečević, Mladen, Hasenstab, Kyle, Cunha, Guilherme Moura. Liver Vaculature Segmentation (LiVaS) [Internet]. Zenodo; 2023 [cited 2023 May 31]. Available from: https://zenodo.org/record/7989974

  16. Dice LR. Measures of the Amount of Ecologic Association Between Species. Ecology. 1945 Jul;26(3):297–302.

    Article  Google Scholar 

  17. Reinke A, Tizabi MD, Sudre CH, Eisenmann M, Rädsch T, Baumgartner M, et al. Common Limitations of Image Processing Metrics: A Picture Story [Internet]. arXiv; 2022 [cited 2022 Dec 5]. Available from: http://arxiv.org/abs/2104.05642

  18. Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015 Dec;15(1):29.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Joskowicz L, Cohen D, Caplan N, Sosna J. Inter-observer variability of manual contour delineation of structures in CT. Eur Radiol. 2019 Mar;29(3):1391–9.

    Article  PubMed  Google Scholar 

  20. Ivashchenko OV, Rijkhorst EJ, Ter Beek LC, Hoetjes NJ, Pouw B, Nijkamp J, et al. A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images. Magn Reson Imaging. 2020 May;68:53–65.

    Article  PubMed  Google Scholar 

  21. Zbinden L, Catucci D, Suter Y, Berzigotti A, Ebner L, Christe A, et al. Convolutional neural network for automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon acquisitions. Sci Rep. 2022 Dec 21;12(1):22059.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Oh N, Kim JH, Rhu J, Jeong WK, Choi GS, Kim JM, Joh JW. Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning. Sci Rep. 2023 Oct 17;13(1):17605.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Tang A, Chen J, Le TA, Changchien C, Hamilton G, Middleton MS, et al. Cross-sectional and longitudinal evaluation of liver volume and total liver fat burden in adults with nonalcoholic steatohepatitis. Abdom Imaging. 2015 Jan;40(1):26–37.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work has been primarily funded by the RSNA R&E Foundation Fellow Grant 04 (RF2104). Partial financial support to one of the coauthors (MZ) was also provided by the NIH grant P50CA228944.

Author information

Author notes

  1. Mladen Zecevic and Kyle A. Hasenstab contributed equally to this work as first authors.

Authors and Affiliations

  1. Department of Radiology, University of Washington, 1705 NE Pacific St, BB308, Seattle, WA, 98195, USA

    Mladen Zecevic, Manish Dhyani & Guilherme Moura Cunha

  2. Department of Mathematics and Statistics, San Diego State University, San Diego, CA, USA

    Kyle A. Hasenstab

  3. Department of Radiology, Stanford University, Stanford, CA, USA

    Kang Wang

Authors
  1. Mladen Zecevic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kyle A. Hasenstab
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manish Dhyani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guilherme Moura Cunha
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed in collaboration by all authors. The first draft of the manuscript was written by Mladen Zecevic and Kyle Hasentab, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Guilherme Moura Cunha.

Ethics declarations

Ethics Approval

This study was approved by our Institutional Review Board (IRB# STUDY00013546) with a waived requirement for informed consent.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 15 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zecevic, M., Hasenstab, K.A., Wang, K. et al. Signal Intensity Trajectories Clustering for Liver Vasculature Segmentation and Labeling (LiVaS) on Contrast-Enhanced MR Images: A Feasibility Pilot Study. J Digit Imaging. Inform. med. 37, 873–883 (2024). https://doi.org/10.1007/s10278-024-00970-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-024-00970-w

Keywords

Search

Navigation