MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation

Farshad A, Makarevich A, Belagiannis V, Navab N (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13542 LNCS

Pages Range: 45-55

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Singapore, SGP

ISBN: 9783031168512

DOI: 10.1007/978-3-031-16852-9_5

Abstract

The lack of sufficient annotated image data is a common issue in medical image segmentation. For some organs and densities, the annotation may be scarce, leading to poor model training convergence, while other organs have plenty of annotated data. In this work, we present MetaMedSeg, a gradient-based meta-learning algorithm that redefines the meta-learning task for the volumetric medical data with the goal of capturing the variety between the slices. We also explore different weighting schemes for gradients aggregation, arguing that different tasks might have different complexity and hence, contribute differently to the initialization. We propose an importance-aware weighting scheme to train our model. In the experiments, we evaluate our method on the medical decathlon dataset by extracting 2D slices from CT and MRI volumes of different organs and performing semantic segmentation. The results show that our proposed volumetric task definition leads to up to 30 % improvement in terms of IoU compared to related baselines. The proposed update rule is also shown to improve the performance for complex scenarios where the data distribution of the target organ is very different from the source organs. (Project page: http://metamedseg.github.io/ )

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Otto-von-Guericke-Universität Magdeburg DE Germany (DE)

How to cite

APA:

Farshad, A., Makarevich, A., Belagiannis, V., & Navab, N. (2022). MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation. In Konstantinos Kamnitsas, Lisa Koch, Mobarakol Islam, Ziyue Xu, Jorge Cardoso, Qi Dou, Nicola Rieke, Sotirios Tsaftaris (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 45-55). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.

MLA:

Farshad, Azade, et al. "MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation." Proceedings of the 4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, SGP Ed. Konstantinos Kamnitsas, Lisa Koch, Mobarakol Islam, Ziyue Xu, Jorge Cardoso, Qi Dou, Nicola Rieke, Sotirios Tsaftaris, Springer Science and Business Media Deutschland GmbH, 2022. 45-55.

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