Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data.
Mar 14, 2018
Oct 10, 2019 · The predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters.
PDF | Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness.
Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the ...
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On the Applicability of Registration Uncertainty · List of references · Publications that cite this publication.
Jan 28, 2020 · On the applicability of registration uncertainty ; Professeur. Toews, Matthew · Génie des systèmes · 17 déc. 2019 21:23 · 28 janv. 2020 16:24 · https ...
In this paper, we propose an approach that enables accurate voxel-wise deformable registration of high-resolution 3D images without the need for intermediate ...
Aug 14, 2022 · This paper studies a deep learning framework that can quantify and reduce the registration uncertainty of training labels as well as train neural network ...
Dec 13, 2023 · This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications.
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Mar 8, 2024 · Generally, learning-based image registration algorithms consider two types of uncertainties—transformation and appearance uncertainties [5] .