Skip to main content
SpringerLink
Log in

Challenges and Constraints in Deformation-Based Medical Mesh Representation

  • Conference paper
  • First Online:
Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14498))

Included in the following conference series:

  • 241 Accesses

Abstract

Mesh representation of medical imaging isosurfaces are essential for medical analysis. These representations are typically obtained using mesh extraction methods to segment 3D volumes. However, the meshes extracted from such methods often suffer from undesired staircase artefacts. In this paper, we evaluate the existing mesh deformation methods that deform a template mesh to desired shapes. We evaluate two variants of such method on three datasets of varying topological complexity. Our objective is to demonstrate that, despite the mesh deformation methods having their limitations, they avoid the generation of staircase artefacts.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Meyer-Spradow, J., et al.: Voreen: a rapid-prototyping environment for ray-casting-based volume visualizations. IEEE Comput. Graphics Appl. 29(6), 6–13 (2009)

    Article  Google Scholar 

  2. Wickramasinghe, U., Remelli, E., Knott, G., Fua, P.: Voxel2mesh: 3d mesh model generation from volumetric data. In: Martel, A.L., et al. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV, pp. 299–308. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_30

    Chapter  Google Scholar 

  3. Li, W., Hahn, J.K.: Efficient ray casting polygonized isosurface of binary volumes. Vis. Comput. 37(12), 3139–3149 (2021). https://doi.org/10.1007/s00371-021-02302-3

    Article  Google Scholar 

  4. Kong, F., Shadden, S.C.: Whole heart mesh generation for image-based computational simulations by learning free-from deformations. In: de Bruijne, Marleen, et al. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part IV, pp. 550–559. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87202-1_53

    Chapter  Google Scholar 

  5. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  6. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49

    Chapter  Google Scholar 

  7. Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. ACM siggraph Comput. Graph. 21(4), 163–169 (1987)

    Article  Google Scholar 

  8. Moench, T., et al.: Context-aware mesh smoothing for biomedical applications. Comput. Graph. 35(4), 755–767 (2011)

    Article  Google Scholar 

  9. Wang, N., Zhang, Y., Li, Z., Yanwei, F., Liu, W., Jiang, Y.-G.: Pixel2mesh: generating 3d mesh models from single RGB images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 55–71. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_4

    Chapter  Google Scholar 

  10. Wen, C., et al.: Pixel2mesh++: multi-view 3D mesh generation via deformation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)

    Google Scholar 

  11. Liu, P., et al.: Deep learning to segment pelvic bones: large-scale CT datasets and baseline models. Int. J. Comput. Assist. Radiol. Surg. 16, 749–756 (2021). https://doi.org/10.1007/s11548-021-02363-8

    Article  Google Scholar 

  12. Kavur, A.E., et al.: CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation. Med. Image Anal. 69, 101950 (2021)

    Article  Google Scholar 

  13. Rister, B., et al.: CT-ORG, a new dataset for multiple organ segmentation in computed tomography. Sci. Data 7(1), 381 (2020)

    Article  Google Scholar 

  14. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  15. Zhao, H., et al.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  16. Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: Stoyanov, Danail, et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1

    Chapter  Google Scholar 

  17. Isensee, F., et al.: NnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)

    Article  Google Scholar 

  18. Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). IEEE (2016)

    Google Scholar 

  19. Liao, Y., Donne, S., Geiger, A.: Deep marching cubes: Learning explicit surface representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  20. Chen, Z., Zhang, H.: Neural marching cubes. ACM Trans. Graph. (TOG) 40(6), 1–15 (2021)

    Article  MathSciNet  Google Scholar 

  21. Liu, R., et al.: TMM-Nets: transferred multi-to mono-modal generation for lupus retinopathy diagnosis. IEEE Trans. Med. Imaging 42(4), 1083–1094 (2022)

    Article  Google Scholar 

  22. Terzopoulos, D., Fleischer, K.: Deformable models. Vis. Comput. 4(6), 306–331 (1988). https://doi.org/10.1007/BF01908877

    Article  Google Scholar 

  23. Terzopoulos, D., Witkin, A., Kass, M.: Constraints on deformable models: recovering 3D shape and nonrigid motion. Artif. Intell. 36(1), 91–123 (1988)

    Article  Google Scholar 

  24. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vision 1(4), 321–331 (1988). https://doi.org/10.1007/BF00133570

    Article  Google Scholar 

  25. Berger, M.-O.: Snake growing. In: Faugeras, O. (ed.) ECCV 1990. LNCS, vol. 427, pp. 570–572. Springer, Heidelberg (1990). https://doi.org/10.1007/BFb0014909

    Chapter  Google Scholar 

  26. Scarselli, F., et al.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)

    Article  Google Scholar 

  27. Lebrat, L., et al.: Corticalflow: a diffeomorphic mesh transformer network for cortical surface reconstruction. Adv. Neural. Inf. Process. Syst. 34, 29491–29505 (2021)

    Google Scholar 

  28. Bongratz, F., et al.: Vox2Cortex: fast explicit reconstruction of cortical surfaces from 3D MRI scans with geometric deep neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  29. Ma, Q., Robinson, E.C., Kainz, B., Rueckert, D., Alansary, A.: PialNN: a fast deep learning framework for cortical pial surface reconstruction. In: Abdulkadir, Ahmed, et al. (eds.) Machine Learning in Clinical Neuroimaging: 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings, pp. 73–81. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87586-2_8

    Chapter  Google Scholar 

  30. Kong, Fanwei, Shadden, Shawn C.: Learning whole heart mesh generation from patient images for computational simulations. IEEE Trans. Med. Imaging 42(2), 533–545 (2022)

    Article  Google Scholar 

  31. Yang, J., et al.: ImplicitAtlas: learning deformable shape templates in medical imaging. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  32. McInemey, T., Terzopoulos, D.: Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Trans. Med. Imaging 18(10), 840–850 (1999)

    Article  Google Scholar 

  33. Sapiro, G., Kimmel, R., Caselles, V.: Object detection and measurements in medical images via geodesic deformable contours. In: Vision Geometry IV. SPIE (1995)

    Google Scholar 

  34. McInerney, T., Terzopoulos, D.: Topologically adaptable snakes. In: Proceedings of IEEE International Conference on Computer Vision. IEEE (1995)

    Google Scholar 

  35. Cignoni, P., et al.: Meshlab: an open-source mesh processing tool. In: Eurographics Italian Chapter Conference, Salerno, Italy (2008)

    Google Scholar 

  36. Heimann, T., et al.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imaging 28(8), 1251–1265 (2009)

    Article  Google Scholar 

  37. Gerig, G., Jomier, M., Chakos, M.: Valmet: a new validation tool for assessing and improving 3D object segmentation. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 516–523. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45468-3_62

    Chapter  Google Scholar 

  38. Borgefors, G.: Distance transformations in digital images. Comput. Vision Graph. Image Process. 34(3), 344–371 (1986)

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by ARC DP200103748.

Author information

Authors and Affiliations

  1. University of Sydney, Camperdown, NSW, 2050, Australia

    Ge Jin & Jinman Kim

  2. Gachon University, Seongnam, Gyeonggi, 13306, Republic of Korea

    Younhyun Jung

Authors
  1. Ge Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Younhyun Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinman Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ge Jin .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneve, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jin, G., Jung, Y., Kim, J. (2024). Challenges and Constraints in Deformation-Based Medical Mesh Representation. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14498. Springer, Cham. https://doi.org/10.1007/978-3-031-50078-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50078-7_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50077-0

  • Online ISBN: 978-3-031-50078-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation