The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties

• URL: http://arxiv.org/abs/2111.02771v1
• Date: Thu, 4 Nov 2021 11:52:49 GMT
• Title: The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties
• Authors: Pedro Borges, Richard Shaw, Thomas Varsavsky, Kerstin Klaser, David Thomas, Ivana Drobnjak, Sebastien Ourselin and M Jorge Cardoso
• Abstract summary: In this paper we demonstrate the efficacy of a physics-informed, uncertainty-aware, segmentation network. We show that the proposed approach also accurately extrapolates to out-of-distribution sequence samples. We demonstrate a significant improvement in terms of coefficients of variation, backed by uncertainty based volumetric validation.
• Score: 3.46329153611365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling [4.76185521514135]
  We introduce a novel method, DiffHybrid-UQ, for effective and efficient uncertainty propagation and estimation in hybrid neural differentiable models. Specifically, our approach effectively discerns and quantifies both aleatoric uncertainties, arising from data noise, and epistemic uncertainties, resulting from model-form discrepancies and data sparsity.
  arXiv  Detail & Related papers  (2023-12-30T07:40:47Z)
• Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation [8.64414399041931]
  Uncertainty quantification (UQ) is an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segmentation. We develop measures for quantifying uncertainty at lesion and patient scales, derived from structural prediction discrepancies. The results from a multi-centric MRI dataset of 334 patients demonstrate that our proposed measures more effectively capture model errors at the lesion and patient scales.
  arXiv  Detail & Related papers  (2023-11-15T13:04:57Z)
• Toward Robust Uncertainty Estimation with Random Activation Functions [3.0586855806896045]
  We propose a novel approach for uncertainty quantification via ensembles, called Random Activation Functions (RAFs) Ensemble. RAFs Ensemble outperforms state-of-the-art ensemble uncertainty quantification methods on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-02-28T13:17:56Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Leveraging Heteroscedastic Uncertainty in Learning Complex Spectral Mapping for Single-channel Speech Enhancement [20.823177372464414]
  Most speech enhancement (SE) models learn a point estimate, and do not make use of uncertainty estimation in the learning process. We show that modeling heteroscedastic uncertainty by minimizing a multivariate Gaussian negative log-likelihood (NLL) improves SE performance at no extra cost.
  arXiv  Detail & Related papers  (2022-11-16T02:29:05Z)
• Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes [52.92110730286403]
  It is commonly believed that the marginal likelihood should be reminiscent of cross-validation metrics and that both should deteriorate with larger input dimensions. We prove that by tuning hyper parameters, the performance, as measured by the marginal likelihood, improves monotonically with the input dimension. We also prove that cross-validation metrics exhibit qualitatively different behavior that is characteristic of double descent.
  arXiv  Detail & Related papers  (2022-10-14T08:09:33Z)
• The Unreasonable Effectiveness of Deep Evidential Regression [72.30888739450343]
  A new approach with uncertainty-aware regression-based neural networks (NNs) shows promise over traditional deterministic methods and typical Bayesian NNs. We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a quantification rather than an exact uncertainty.
  arXiv  Detail & Related papers  (2022-05-20T10:10:32Z)
• BayesIMP: Uncertainty Quantification for Causal Data Fusion [52.184885680729224]
  We study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable. We introduce a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space.
  arXiv  Detail & Related papers  (2021-06-07T10:14:18Z)
• Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction [63.202627467245584]
  We introduce a Bayesian variational framework to quantify the model-immanent (epistemic) uncertainty. We demonstrate that our approach yields competitive results for undersampled MRI reconstruction.
  arXiv  Detail & Related papers  (2021-02-12T18:08:14Z)
• The Hidden Uncertainty in a Neural Networks Activations [105.4223982696279]
  The distribution of a neural network's latent representations has been successfully used to detect out-of-distribution (OOD) data. This work investigates whether this distribution correlates with a model's epistemic uncertainty, thus indicating its ability to generalise to novel inputs.
  arXiv  Detail & Related papers  (2020-12-05T17:30:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.