Abstract
While deep learning models are known to be able to solve the task of multi-organ segmentation, the scarcity of fully annotated multi-organ datasets poses a significant obstacle during training. The 3D volume annotation of such datasets is expensive, time-consuming and varies greatly in the variety of labeled structures. To this end, we propose a solution that leverages multiple partially annotated datasets using disentangled learning for a single segmentation model. Dataset-specific encoder and decoder networks are trained, while a joint decoder network gathers the encoders’ features to generate a complete segmentation mask. We evaluated our method using two simulated partially annotated datasets: one including the liver, lungs and kidneys, the other bones and bladder. Our method is trained to segment all five organs achieving a dice score of 0.78 and an IoU of 0.67. Notably, this performance is close to a model trained on the fully annotated dataset, scoring 0.80 in dice score and 0.70 in IoU respectively.
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References
Aljabri M, AlGhamdi M. A review on the use of deep learning for medical images segmentation. Neurocomputing. 2022;506:311–35.
Hesamian M, Jia W, He X et al. Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging. 2019;32:582–96.
Lei Y, Fu Y, Wang T, Qiu RLJ, Curran WJ, Liu T et al. Deep learning in multi-organ segmentation. 2020.
Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X. A review of deep learning based methods for medical image multi-organ segmentation. Physica Medica. 2021;85:107–22.
Rister B, Yi D, Shivakumar K, Nobashi T, Rubin DL. CT-ORG, a new dataset for multiple organ segmentation in computed tomography. Sci. Data. 2020;7(1):381.
Ji Y, Bai H, Ge C, Yang J, Zhu Y, Zhang R et al. Amos: a large-scale abdominal multi-organ benchmark for versatile medical image segmentation. Proc NeuroIPS. 2022;35:36722–32.
Zhu X. Semi-supervised learning literature survey. Comput Sci. 2008;2.
Pan SJ,Yang Q.Asurvey on transfer learning. IEEETrans Knowl Data Eng. 2010;22(10):1345– 59.
Zhou ZH. A brief introduction to weakly supervised learning. Natl Sci Rev. 2018;5(1):44–53.
Zhou T, Ruan S, Canu S. A review: deep learning for medical image segmentation using multi-modality fusion. Array. 2019;3-4:100004.
Lyu Y, Liao H, Zhu H, Zhou SK. A3DSegNet: anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation. 2021.
Yang Q, Guo X, Chen Z,Woo PYM,Yuan Y. D2-Net: dual disentanglement network for brain tumor segmentation with missing modalities. IEEE Trans Med Imaging. 2022;41(10):2953– 64.
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. Proc MICCAI. 2015:234–41.
Salimi Y, Shiri I, Mansouri Z, Zaidi H. Deep learning-assisted multiple organ segmentation from whole-body CT images. medRxiv. 2023.
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Wang, T., Liu, C., Rist, L., Maier, A. (2024). Multi-organ Segmentation in CT from Partially Annotated Datasets using Disentangled Learning. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_76
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DOI: https://doi.org/10.1007/978-3-658-44037-4_76
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