지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.16 No.1
- 발행연도
- 2022.3
- 수록면
- 43 - 51 (9page)
- DOI
- 10.5626/JCSE.2022.16.1.43
이용수
초록· 키워드
오류제보하기
Learning-based medical image segmentation has been advanced with the collection of datasets and various training methodologies. In this work, we target bone cement (polymethylmethacrylate [PMMA]) inserted vertebral body segmentation, where the target dataset was relatively scarce, compared to a large-scale dataset for the regular vertebra segmentation task. We presented a novel domain transformation framework, where a large-scale training set for our target task was generated from the existing dataset of a different domain. We proposed two main components: label translation and image translation. Label translation was proposed to filter out unnecessary regions in a segmentation map for our target task. In addition to label translation, image translation was proposed to virtually generate PMMA-inserted images from the original data. The synthesized training set by our method successfully simulated the data distribution of the target task; therefore a clear performance improvement was observed by the dataset. By extensive experiments, we showed that our method outperformed baseline methods in terms of segmentation performance. In addition, a more accurate shape and volume of a bone were measured by our method, which satisfied the medical purpose of segmentation.
목차
Abstract
I. INTRODUCTION
II. RELATED WORK
III. METHODS
IV. EXPERIMENTS
V. CONCLUSION
REFERENCES
참고문헌 (20)
참고문헌 신청
A. Hatamizadeh, D. Yang, H. Roth, and D. Xu, “Unetr: transformers for 3D medical image segmentation,” 2021 [Online]. Available: https://arxiv.org/abs/2103.10504.
C. Payer, D. Stern, H. Bischof, and M. Urschler, “Coarse to fine vertebrae localization and segmentation with SpatialConfiguration-Net and U-Net,” in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), Valletta, Malta, 2020, pp. 124-133.
O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: learning dense volumetric segmentation from sparse annotation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Cham, Switzerland: Springer, 2016, pp. 424-432.
B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al, “The multimodal brain tumor image segmentation benchmark (BRATS),” IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 1993-2024, 2014.
A. Sekuboyina, M. E. Husseini, A. Bayat, M. Loffler, H. Liebl, H. Li, et al., “VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images,” Medical Image Analysis, vol. 73, article no. 102166, 2021. https://doi.org/10.1016/j.media.2021.102166
O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: learning dense volumetric segmentation from sparse annotation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Cham, Switzerland: Springer, 2016, pp. 424-432.
B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al, “The multimodal brain tumor image segmentation benchmark (BRATS),” IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 1993-2024, 2014.
A. Sekuboyina, M. E. Husseini, A. Bayat, M. Loffler, H. Liebl, H. Li, et al., “VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images,” Medical Image Analysis, vol. 73, article no. 102166, 2021. https://doi.org/10.1016/j.media.2021.102166
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