Abstract
Medical image deep learning segmentation has shown great potential in becoming an ubiquitous part of the clinical analysis pipeline. However, these methods cannot guarantee high quality predictions when the source and target domains are dissimilar due to different acquisition protocols, or biases in patient cohorts. Recently, unsupervised domain adaptation techniques have shown great potential in alleviating this problem by minimizing the shift between the source and target distributions. In this work, we aim to predict tissue segmentation maps on an unseen dataset, which has both different acquisition parameters and population bias when compared to our training data. We achieve this by investigating two unsupervised domain adaptation (UDA) techniques with the objective of finding the best solution for our problem. We compare the two methods with a baseline fully-supervised segmentation network in terms of cortical thickness measures.
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Acknowledgments
This work was supported by the Academy of Medical Sciences Springboard Award (SBF004\({\backslash }\)1040), European Research Council under the European Union’s Seventh Framework Programme (FP7/ 20072013)/ERC grant agreement no. 319456 dHCP project, the Wellcome/EPSRC Centre for Medical Engineering at King’s College London (WT 203148/Z/16/Z), the NIHR Clinical Research Facility (CRF) at Guy’s and St Thomas’ and by the National Institute for Health Research Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
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Grigorescu, I. et al. (2020). Harmonised Segmentation of Neonatal Brain MRI: A Domain Adaptation Approach. In: Hu, Y., et al. Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis. ASMUS PIPPI 2020 2020. Lecture Notes in Computer Science(), vol 12437. Springer, Cham. https://doi.org/10.1007/978-3-030-60334-2_25
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DOI: https://doi.org/10.1007/978-3-030-60334-2_25
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