Abstract
This paper proposes enhancement filters for shape-specific regions, based on radial structure tensor (RST) analysis, which we name “spaciousness filters”. RST analysis can be used in a similar way to Hessian analysis for classifying intensity structures. However, RST is insufficient for enhancing regions having little contrast or non-typical morphology. Our proposed filters enhance such regions by extending the ray search scheme of RST analysis to work as a filter evaluating spaciousness. We show applications to the abdominal CT of ileus patients having specific shapes. The intestines (including small intestines) of those patients consist of air, liquid and feces portions, and are not contrast-enhanced by barium. Enhancement of liquid and walls play key roles in the sufficient segmentation of intestines and division between neighboring regions. Experimental results on 7 clinical cases showed that the proposed intestine segmentation method produced higher Dice score (0.68) than traditional RST analysis (0.44), even without specific refinement processes like machine-learning-based false positive reduction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wyatt, C., Ge, Y., Vining, D.: Automatic segmentation of the colon for virtual colonoscopy. Comput. Med. Imaging Graph. 24(1), 1–9 (2000)
Tulum, G., Bolat, B., Osman, O.: A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans. Int. J. Comput. Assist. Radiol. Surg. 12(4), 627–644 (2017). https://doi.org/10.1007/s11548-017-1521-9
Tachibana, R., et al.: Deep learning electronic cleansing for single- and dual-energy CT colonography. RadioGraphics 38(7), 2034–2050 (2018). PMID: 30422761
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056195
Arseneau, S., Cooperstock, J.R.: An asymmetrical diffusion framework for junction analysis. In: BMVC, pp. 689–698 (2006)
Arseneau, S., Cooperstock, J.R.: An improved representation of junctions through asymmetric tensor diffusion. In: Bebis, G., et al. (eds.) ISVC 2006. LNCS, vol. 4291, pp. 363–372. Springer, Heidelberg (2006). https://doi.org/10.1007/11919476_37
Wiemker, R., Klinder, T., Bergtholdt, M., Meetz, K., Carlsen, I.C., Bülow, T.: A radial structure tensor and its use for shape-encoding medical visualization of tubular and nodular structures. IEEE Trans. Visual Comput. Graphics 19(3), 353–366 (2013)
Sato, Y., et al.: Tissue classification based on 3D local intensity structures for volume rendering. IEEE TVCG 6(2), 160–180 (2000)
Acknowledgements
Parts of this work were supported by the Hori Sciences & Arts Foundation, MEXT/JSPS KAKENHI (17H00867, 17K20099, 26108006, 26560255), JSPS Bilateral Joint Research Projects and AMED (19lk1010036h0001).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Oda, H. et al. (2019). Spaciousness Filters for Non-contrast CT Volume Segmentation of the Intestine Region for Emergency Ileus Diagnosis. In: Greenspan, H., et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures. CLIP UNSURE 2019 2019. Lecture Notes in Computer Science(), vol 11840. Springer, Cham. https://doi.org/10.1007/978-3-030-32689-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-32689-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32688-3
Online ISBN: 978-3-030-32689-0
eBook Packages: Computer ScienceComputer Science (R0)
