Abstract
Segmentation of organs and tumors from abdominal computed tomography (CT) scans is crucial for cancer diagnosis and surgical planning. Since traditional segmentation methods are subjective and labor-intensive, deep learning-based approaches have been introduced recently which incur high computational costs. This study proposes an accurate and efficient segmentation method for abdominal organs and tumors in CT images utilizing a partially-labeled abdominal CT dataset. Fine nnU-Net was used for the pseudo-labeling of unlabeled images. And the Label Fusion Algorithm combined the benefits of the provided datasets to build an optimal training dataset, using Swift nnU-Net for efficient inference. In online validation using Swift nnU-Net, the dice similarity coefficient (DSC) values for organs and tumors segmentation were 89.56% and 35.70%, respectively, and the normalized surface distance (NSD) values were 94.67% and 25.52%. In our own efficiency experiments, the inference time was an average of 10.7 s and the area under the GPU memory time curve was an average of 20316.72 MB. Our method enables accurate and efficient segmentation of abdominal organs and tumors using partially labeled data, unlabeled data, and pseudo-labels. This method could be applied to multi-organ and pan-cancer segmentation in abdominal CT images under low-resource environments.
This is a preview of subscription content, log in via an institution to check access.
References
Bilic, P., et al.: The liver tumor segmentation benchmark (LiTs). Med. Image Anal. 84, 102680 (2023)
Chen, S., Ma, K., Zheng, Y.: Med3d: transfer learning for 3D medical image analysis. ar**v preprint ar**v:1904.00625 (2019)
Chetty, G., Yamin, M., White, M.: A low resource 3D U-net based deep learning model for medical image analysis. Int. J. Inf. Technol. 14(1), 95–103 (2022)
Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)
Fu, Y., Lei, Y., Wang, T., Curran, W.J., Liu, T., Yang, X.: A review of deep learning based methods for medical image multi-organ segmentation. Physica Med. 85, 107–122 (2021)
Gatidis, S., et al.: The autoPET challenge: towards fully automated lesion segmentation in oncologic PET/CT imaging (2023)
Gatidis, S.: A whole-body FDG-PET/CT dataset with manually annotated tumor lesions. Sci. Data 9(1), 601 (2022)
Gibson, E., et al.: Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans. Med. Imaging 37(8), 1822–1834 (2018)
Gul, S., Khan, M.S., Bibi, A., Khandakar, A., Ayari, M.A., Chowdhury, M.E.: Deep learning techniques for liver and liver tumor segmentation: a review. Comput. Biol. Med. 147, 105620 (2022)
Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022)
Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the kits19 challenge. Med. Image Anal. 67, 101821 (2021)
Heller, N., et al.: An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in CT imaging. Proc. Am. Soc. Clin. Oncol. 38(6), 626 (2020)
Huang, Z., et al,: Revisiting nnU-Net for iterative pseudo labeling and efficient sliding window inference. In: Ma, J., Wang, B. (eds.) Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation. FLARE 2022. LNCS, vol. 13816, pp. 178–189. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-23911-3_16
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Ji, Y., et al.: AMOS: a large-scale abdominal multi-organ benchmark for versatile medical image segmentation. In: Advances in Neural Information Processing Systems, vol. 35, pp. 36722–36732 (2022)
Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024)
Ma, J., et al.: Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge. Med. Image Anal. 82, 102616 (2022)
Ma, J., et al.: Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the flare22 challenge. ar**v preprint ar**v:2308.05862 (2023)
Ma, J., et al.: Abdomenct-1k: is abdominal organ segmentation a solved problem? IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6695–6714 (2022)
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), pp. 565–571. IEEE (2016)
Pavao, A., et al.: Codalab competitions: an open source platform to organize scientific challenges. J. Mach. Learn. Res. 24(198), 1–6 (2023)
Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. ar**v preprint ar**v:1902.09063 (2019)
Wang, E., Zhao, Y., Wu, Y.: Cascade dual-decoders network for abdominal organs segmentation. In: Ma, J., Wang, B. (eds.) Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation. FLARE 2022. LNCS, vol. 13816, pp. 202–213. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-23911-3_18
Wasserthal, J., et al.: Totalsegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5(5), e230024 (2023)
**a, H., Sun, W., Song, S., Mou, X.: MD-Net: multi-scale dilated convolution network for CT images segmentation. Neural Process. Lett. 51, 2915–2927 (2020)
Yesilkaynak, V.B., Sahin, Y.H., Unal, G.: EfficientSeg: an efficient semantic segmentation network. ar**v preprint ar**v:2009.06469 (2020)
Yushkevich, P.A., Gao, Y., Gerig, G.: ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3342–3345 (2016)
Zhang, J., **e, Y., **a, Y., Shen, C.: DoDNet: learning to segment multi-organ and tumors from multiple partially labeled datasets. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1195–1204 (2021)
Acknowledgements
We declare that the segmentation method we implemented for participation in the FLARE 2023 challenge has not used any pre-trained models nor additional datasets other than those provided by the organizers. The proposed solution is fully automatic without any manual intervention. We thank all the data owners for making the CT scans publicly available and CodaLab [21] for hosting the challenge platform.
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-0052, Cloud-based XR content conversion and service technology development that changes according to device performance) and Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea Government (MSIT) (No. 2021-0-00312, development of non-face-to-face patient infection activity prediction and protection management SW technology at home and community treatment centers for effective response to infectious disease).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kong, Y., Kim, K., Jeong, S., Lee, K.E., Kong, HJ. (2024). Multi-Organ and Pan-Cancer Segmentation Framework from Partially Labeled Abdominal CT Datasets: Fine and Swift nnU-Nets with Label Fusion. In: Ma, J., Wang, B. (eds) Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT. FLARE 2023. Lecture Notes in Computer Science, vol 14544. Springer, Cham. https://doi.org/10.1007/978-3-031-58776-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-58776-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-58775-7
Online ISBN: 978-3-031-58776-4
eBook Packages: Computer ScienceComputer Science (R0)