From Registration Uncertainty to Segmentation Uncertainty

• URL: http://arxiv.org/abs/2403.05111v1
• Date: Fri, 8 Mar 2024 07:16:14 GMT
• Title: From Registration Uncertainty to Segmentation Uncertainty
• Authors: Junyu Chen, Yihao Liu, Shuwen Wei, Zhangxing Bian, Aaron Carass, Yong Du
• Abstract summary: We propose a novel framework to concurrently estimate both the epistemic and aleatoric segmentation uncertainties for image registration. By introducing segmentation uncertainty along with existing methods for estimating registration uncertainty, we offer vital insights into the potential uncertainties at different stages of image registration.
• Score: 11.294691606431526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the uncertainty inherent in deep learning-based image registration models has been an ongoing area of research. Existing methods have been developed to quantify both transformation and appearance uncertainties related to the registration process, elucidating areas where the model may exhibit ambiguity regarding the generated deformation. However, our study reveals that neither uncertainty effectively estimates the potential errors when the registration model is used for label propagation. Here, we propose a novel framework to concurrently estimate both the epistemic and aleatoric segmentation uncertainties for image registration. To this end, we implement a compact deep neural network (DNN) designed to transform the appearance discrepancy in the warping into aleatoric segmentation uncertainty by minimizing a negative log-likelihood loss function. Furthermore, we present epistemic segmentation uncertainty within the label propagation process as the entropy of the propagated labels. By introducing segmentation uncertainty along with existing methods for estimating registration uncertainty, we offer vital insights into the potential uncertainties at different stages of image registration. We validated our proposed framework using publicly available datasets, and the results prove that the segmentation uncertainties estimated with the proposed method correlate well with errors in label propagation, all while achieving superior registration performance.

Related papers

• Hierarchical uncertainty estimation for learning-based registration in neuroimaging [10.964653898591413]
  We propose a principled way to propagate uncertainties (epistemic or aleatoric) estimated at the level of spatial location. Experiments show that uncertainty-aware fitting of transformations improves the registration accuracy of brain MRI scans.
  arXiv  Detail & Related papers  (2024-10-11T23:12:16Z)
• Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration [32.081258147692395]
  We propose a framework for heteroscedastic image uncertainty estimation. It can adaptively reduce the influence of regions with high uncertainty during unsupervised registration. Our method consistently outperforms baselines and produces sensible uncertainty estimates.
  arXiv  Detail & Related papers  (2023-12-01T01:03:06Z)
• Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks [101.56637264703058]
  We show that a variational Bayesian neural network approach can be used to improve uncertainty estimates. We propose a new measure of uncertainty for contrastive learning, that is based on the disagreement in likelihood due to different positive samples.
  arXiv  Detail & Related papers  (2023-11-30T22:32:24Z)
• Towards Better Certified Segmentation via Diffusion Models [62.21617614504225]
  segmentation models can be vulnerable to adversarial perturbations, which hinders their use in critical-decision systems like healthcare or autonomous driving. Recently, randomized smoothing has been proposed to certify segmentation predictions by adding Gaussian noise to the input to obtain theoretical guarantees. In this paper, we address the problem of certifying segmentation prediction using a combination of randomized smoothing and diffusion models.
  arXiv  Detail & Related papers  (2023-06-16T16:30:39Z)
• Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning [1.6752182911522522]
  We show that uncertainty sampling is sufficient to achieve exploration versus exploitation in graph-based active learning. In particular, we use a recently developed algorithm, Poisson ReWeighted Laplace Learning (PWLL) for the classifier. We present experimental results on a number of graph-based image classification problems.
  arXiv  Detail & Related papers  (2022-10-27T22:07:53Z)
• Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model [68.34559610536614]
  We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model. We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation. Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
  arXiv  Detail & Related papers  (2021-11-22T08:54:10Z)
• Training on Test Data with Bayesian Adaptation for Covariate Shift [96.3250517412545]
  Deep neural networks often make inaccurate predictions with unreliable uncertainty estimates. We derive a Bayesian model that provides for a well-defined relationship between unlabeled inputs under distributional shift and model parameters. We show that our method improves both accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2021-09-27T01:09:08Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)
• 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)
• Probabilistic Deep Learning for Instance Segmentation [9.62543698736491]
  We propose a generic method to obtain model-inherent uncertainty estimates within proposal-free instance segmentation models. We evaluate our method on the BBBC010 C. elegans dataset, where it yields competitive performance.
  arXiv  Detail & Related papers  (2020-08-24T19:51:48Z)

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.