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The Role of MRI Physics in Brain Segmentation CNNs: Achieving Acquisition Invariance and Instructive Uncertainties

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12965)


Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases. It is important therefore to design algorithms that are not only robust to images of differing contrasts, but also be able to generalise well to unseen ones, with a quantifiable measure of uncertainty. In this paper we demonstrate the efficacy of a physics-informed, uncertainty-aware, segmentation network that employs augmentation-time MR simulations and homogeneous batch feature stratification to achieve acquisition invariance. We show that the proposed approach also accurately extrapolates to out-of-distribution sequence samples, providing well calibrated volumetric bounds on these. We demonstrate a significant improvement in terms of coefficients of variation, backed by uncertainty based volumetric validation.

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This project was funded by the Wellcome Flagship Programme (WT213038/Z/18/Z) and Wellcome EPSRC CME (WT203148/Z/16/Z).

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Correspondence to Pedro Borges .

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Borges, P. et al. (2021). The Role of MRI Physics in Brain Segmentation CNNs: Achieving Acquisition Invariance and Instructive Uncertainties. In: Svoboda, D., Burgos, N., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2021. Lecture Notes in Computer Science(), vol 12965. Springer, Cham.

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  • Print ISBN: 978-3-030-87591-6

  • Online ISBN: 978-3-030-87592-3

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