تقرير
The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties
| العنوان: | The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties |
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| المؤلفون: | Borges, Pedro, Shaw, Richard, Varsavsky, Thomas, Klaser, Kerstin, Thomas, David, Drobnjak, Ivana, Ourselin, Sebastien, Cardoso, M Jorge |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Medical Physics |
| الوصف: | 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. Comment: 10 pages, 3 figures, published in: Simulation and Synthesis in Medical Imaging 6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-030-87592-3_7 |
| الوصول الحر: | http://arxiv.org/abs/2111.02771Test |
| رقم الانضمام: | edsarx.2111.02771 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-030-87592-3_7 |
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