Abstract
Medical image segmentation is indicated in a number of treatments and procedures, such as detecting pathological changes and organ resection. However, it is a time-consuming process when done manually. Automatic segmentation algorithms like deep learning methods overcome this hurdle, but they are data-hungry and require expert ground-truth annotations, which is a limitation, particularly in medical datasets. On the other hand, unannotated medical datasets are easier to come by and can be used in several methods to learn ground-truth masks. In this paper, we aim to utilize across-modalities transfer learning to leverage the knowledge learned on a large publicly available and expertly annotated computed tomography (CT) dataset to a small unannotated dataset in a different modality magnetic resonance (MR). Moreover, we prove that quickly generated weak annotations can be improved iteratively using a pre-trained U-Net model and will approach the ground truth masks through iterations. This methodology was proven qualitatively using an in-house MR dataset where professionals were asked to choose between model output and weak annotations. They chose model output 93% \(\sim \) 94% of the time. Moreover, we prove it quantitatively using the publicly available annotated Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) dataset. The weak annotation showed improvements across three iterations from 87.5% to 92.2% Dice score when compared to the ground truth annotations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baues, M.: Fibrosis imaging: Current concepts and future directions. Adv. Drug Deliv. Rev.121, 9–26, 2017. ISSN 0169–409X. https://doi.org/10.1016/j.addr.2017.10.013. Fibroblasts and extracellular matrix: Targeting and therapeutic tools in fibrosis and cancer
Bataller, R., Brenner, D.: Liver fibrosis. J. Clin. Invest. 115, 209–18 (2005). https://doi.org/10.1172/JCI24282
Tanaka, M., Miyajima, A.: Liver regeneration and fibrosis after inflammation. Inflamm. Regeneration 36, 12 (2016). https://doi.org/10.1186/s41232-016-0025-2
Moon, A., Singal, A., Tapper, E.: Contemporary epidemiology of chronic liver disease and cirrhosis. Clin. Gastroenterol. Hepatol. 18, 08 (2019). https://doi.org/10.1016/j.cgh.2019.07.060
Acharya, P., Chouhan, K., Weiskirchen, S., Weiskirchen, R.: Cellular mechanisms of liver fibrosis. Front. Pharmacol. 12 (2021). ISSN 1663–9812. https://doi.org/10.3389/fphar.2021.671640
Gotra, A., et al.: Liver segmentation: indications, techniques and future directions. Insights Imaging 8, 06 (2017). https://doi.org/10.1007/s13244-017-0558-1
Gul, S., Khan, M.S., Bibi, A., Khandakar, A., Ayari, M., Chowdhury, M.: Deep learning techniques for liver and liver tumor segmentation: a review. Comput. Bio. Med. 147, 105620 (2022). https://doi.org/10.1016/j.compbiomed.2022.105620
LeCun, Y. Bengio, Y., Geoffrey Hinton, G.: Deep learning. Nature 521, 436–44 (2015). https://doi.org/10.1038/nature14539
Aggarwal, C.C.: Neural networks and deep learning (2018)
Janiesch, C., Zschech, P., Heinrich, K.: Machine learning and deep learning. Electron. Mark. 31(3), 685–695 (2021)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017). ISSN 1361–8415. https://doi.org/10.1016/j.media.2017.07.005.
Willemink, M.J., et al.: Preparing medical imaging data for machine learning. Radiology 295(1), 4–15 (2020). PMID: 32068507
Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J., Wu, Z., Ding, X.: Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med. Image Anal. 63, 101693 (2020). https://doi.org/10.1016/j.media.2020.101693
Zhang, L., et al.: When unseen domain generalization is unnecessary? rethinking data augmentation (2019)
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
Wang, K., et al.: Automated CT and MRI liver segmentation and biometry using a generalized convolutional neural network. Radiology: Artif. Intell. 1, 180022 (2019). https://doi.org/10.1148/ryai.2019180022
Zhou, B., Augenfeld, Z., Chapiro, S.J., Zhou, K., Liu, C., Duncan, J.S.: Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration. Med. Image Anal. 71, 102041 (2021). ISSN 1361–8415. https://doi.org/10.1016/j.media.2021.102041
Bilic, P., et al.: The liver tumor segmentation benchmark (lits). arxiv:1901.04056 (2019)
Kavur, A.E., Selver, M.A., Dicle, O., Barış, M., Gezer, N.S.: CHAOS - combined (CT-MR) healthy abdominal organ segmentation challenge data (2019). https://doi.org/10.5281/zenodo.3431873
Zhang, L., Gopalakrishnan, V., Lu, L., Summers, R.M., Moss, J., Yao, J.: Self-learning to detect and segment cysts in lung CT images without manual annotation. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1100–1103 (2018). https://doi.org/10.1109/ISBI.2018.8363763
Zhang, T., Yu, L., Hu, N., Lv, S., Gu, S.: Robust Medical Image Segmentation from Non-expert Annotations with Tri-network. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 249–258. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_25
Getao, D., Cao, X., Liang, J., Chen, X., Zhan, Y.: Medical image segmentation based on U-Net: a review. J. Imaging Sci. Technol. 64, 03 (2020). https://doi.org/10.2352/J.ImagingSci.Technol.2020.64.2.020508
Siddique, N., Paheding, S., Elkin, C.P., Devabhaktuni, V.: U-net and its variants for medical image segmentation: a review of theory and applications. IEEE Access 9, 82031–82057 (2021). https://doi.org/10.1109/ACCESS.2021.3086020
MeVis Medical Solutions AG. Mevislab logo (2008). https://www.mevislab.de/
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings, pp. 958–963 (2003). https://doi.org/10.1109/ICDAR.2003.1227801
Kass, M., Witkin, A.P., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vision 1, 321–331 (2004)
Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations. J. Comput. Phys. 79(1): 12–49 (1988). ISSN 0021–9991. https://doi.org/10.1016/0021-9991(88)90002-2
Bertels, J., et al.: Optimizing the dice score and jaccard index for medical image segmentation: theory and practice. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 92–100. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_11
Acknowledgment
This project was supported by the Science and Technology Fund Institute (STDF), Project ID 45891- EG-US Cycle 20.
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
Salah, P.E. et al. (2024). Iterative Refinement Algorithm for Liver Segmentation Ground-Truth Generation Using Fine-Tuning Weak Labels for CT and Structural MRI. In: Waiter, G., Lambrou, T., Leontidis, G., Oren, N., Morris, T., Gordon, S. (eds) Medical Image Understanding and Analysis. MIUA 2023. Lecture Notes in Computer Science, vol 14122. Springer, Cham. https://doi.org/10.1007/978-3-031-48593-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-48593-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48592-3
Online ISBN: 978-3-031-48593-0
eBook Packages: Computer ScienceComputer Science (R0)