Abstract
This paper proposes an intestine segmentation method from CT volumes for the intestinal obstruction and the ileus diagnosis assistance. The previous method was built based on the 3D U-Net, whose computational cost was high. Nevertheless, there was no confirmation that the 3-dimensional network contributed to the segmentation performance. In this paper, we propose a method utilizing the 2D U-Net on behalf of the previous methods’ 3D U-Net. Experimental results using 110 CT volumes showed that both the proposed (2D U-Net) and previous (3D U-Net) methods achieved similar scores for the segmentation accuracy and system usefulness. In addition, the proposed method’s inference was 0.5 min on average, around 8-times faster than the 3D U-Net’s. Although subsequent processes still require more than 20 min for each case, utilizing lightweight networks is essential for practical use in the future, especially for emergency diagnosis.
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References
Shah, Z.K., Uppot, R.N., Wargo, J.A., Hahn, P.F., Sahani, D.V.: Small bowel obstruction: the value of coronal reformatted images from 16-multidetector computed tomography-a clinicoradiological perspective. J. Comput. Assist. Tomogr. 32(1), 23–31 (2008)
Sainani, N.I., Näppi, J., Sahani, D.V., Yoshida, H.: Computer-aided detection of small bowel strictures for emergency radiology in CT enterography. In: Yoshida, H., Cai, W. (eds.) ABD-MICCAI 2010. LNCS, vol. 6668, pp. 91–97. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25719-3_13
Zhang, W., Liu, J., Yao, J., Louie, A., Nguyen, T.B., Wank, S., Nowinski, W.L., Summers, R.M.: Mesenteric vasculature-guided small bowel segmentation on 3-D CT. IEEE Trans. Med. Imaging 32(11), 2006–2021 (2013)
Shin, S.Y., Lee, S., Elton, D., Gulley, J.L., Summers, R.M., et al.: Deep small bowel segmentation with cylindrical topological constraints. In: Martel, A.L. (ed.) MICCAI 2020. LNCS, vol. 12264, pp. 207–215. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_21
Oda, H., et al.: Intestinal region reconstruction of ileus cases from 3D CT images based on graphical representation and its visualization. In: Medical Imaging 2021: Computer-Aided Diagnosis, vol. 11597, p. 115971N. International Society for Optics and Photonics (2021)
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.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
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.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Acknowledgments
Parts of this work were supported by the Hori Sciences and Arts Foundation, MEXT/JSPS KAKENHI (17H00867, 17K20099, 26108006, 26560255), JSPS Bilateral Joint Research Project, AMED (JP19lk1010036, JP20lk1010036), and JST CREST (JPMJCR20D5).
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Oda, H. et al. (2021). Intestine Segmentation with Small Computational Cost for Diagnosis Assistance of Ileus and Intestinal Obstruction. In: Oyarzun Laura, C., et al. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning. DCL PPML LL-COVID19 CLIP 2021 2021 2021 2021. Lecture Notes in Computer Science(), vol 12969. Springer, Cham. https://doi.org/10.1007/978-3-030-90874-4_1
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DOI: https://doi.org/10.1007/978-3-030-90874-4_1
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