ArticleProceedings of SPIE--the International Society for Optical Engineering2018
QUADRATIC: Quality of Dice in Registration Circuits.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy.Radiation oncology (London, England) · 2022Article
- A Novel Feature Extraction Algorithm and System for Flexible Integrated Circuit Packaging Substrate.Micromachines · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Image registration involves identification of a transformation to fit a target image to a reference image space. The success of the registration process is vital for correct interpretation of the results of many medical image-processing applications, including multi-atlas segmentation. While there are several validation metrics employed in rigid registration to examine the accuracy of the method, non-rigid registrations (NRR) are validated subjectively in most cases, validated in offline cases, or based on image similarity metrics, all of which have been shown to poorly correlate with true registration quality. In this paper, we model the error for each target scan by expanding on the idea of Assessing Quality Using Image Registration Circuits (AQUIRC), which created a model for error "quality" associated with NRR. In this paper, we model the Dice similarity coefficient (DSC) error in the network, for a more interpretable measure. We test four functional models using a leave-one-out strategy to evaluate the relationship between edge DSC and circuit DSC: linear, quadratic, third order, or multiplicative models. We found that the quadratic model most accurately learns the NRR-DSC, with a median correlation coefficient of 0.58 with the true NRR-DSC, we call this the QUADRATIC (QUAlity of Dice in RegistrATIon Circuits) model. The QUADRATIC model is used for multi-atlas segmentation based on majority vote. Choosing the four best atlases predicted from the QUDRATIC model resulted in a 7% increase in the DSC between segmented image and true labels.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.