2019
DOI: 10.1101/738641
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Automatic Spatial Estimation of White Matter Hyperintensities Evolution in Brain MRI using Disease Evolution Predictor Deep Neural Networks

Abstract: AbstractPrevious studies have indicated that white matter hyperintensities (WMH), the main radiological feature of small vessel disease, may evolve (i.e., shrink, grow) or stay stable over a period of time. Predicting these changes are challenging because it involves some unknown clinical risk factors that leads to a non-deterministic prediction task. In this study, we propose a deep learning model to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later),… Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
18
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
4
1
1

Relationship

1
5

Authors

Journals

citations
Cited by 6 publications
(18 citation statements)
references
References 67 publications
(73 reference statements)
0
18
0
Order By: Relevance
“…Deep generative methods have also been used for this task. While Rachmadi et al (2019Rachmadi et al ( , 2020 and Wegmayr et al (2019) used formulations of Generative Adversarial Networks (GAN) (Goodfellow et al, 2014) to simulate brain changes, others (Ravi et al, 2019a) used a conditional adversarial autoencoder as the generative model, following a recent face ageing approach (Zhang et al, 2017). Irrespective of the model, these methods need longitudinal data, which limits their applicability.…”
Section: Brain Ageing Simulationmentioning
confidence: 99%
See 2 more Smart Citations
“…Deep generative methods have also been used for this task. While Rachmadi et al (2019Rachmadi et al ( , 2020 and Wegmayr et al (2019) used formulations of Generative Adversarial Networks (GAN) (Goodfellow et al, 2014) to simulate brain changes, others (Ravi et al, 2019a) used a conditional adversarial autoencoder as the generative model, following a recent face ageing approach (Zhang et al, 2017). Irrespective of the model, these methods need longitudinal data, which limits their applicability.…”
Section: Brain Ageing Simulationmentioning
confidence: 99%
“…In summary, most previous methods either built average atlases (Zhang et al, 2016;Huizinga et al, 2018;Ziegler et al, 2012;Serag et al, 2012), or required longitudinal data (Rachmadi et al, 2019(Rachmadi et al, , 2020Ravi et al, 2019a;Wegmayr et al, 2019) to simulate brain ageing. Other methods either did not consider subject identity (Bowles et al, 2018;Milana, 2017), or did not evaluate in detail morphological changes (Pawlowski et al, 2020;Zhao et al, 2019).…”
Section: Brain Ageing Simulationmentioning
confidence: 99%
See 1 more Smart Citation
“…In comparison to qualitative visual rating scales, volumetry provides greater accuracy and precision (Gouw et al, 2008; Straaten et al, 2006). The use of machine learning and deep learning algorithms shows promise and can further improve the sensitivity and prognostic accuracy of volumetric analysis (Rachmadi et al, 2020). However, the advanced imaging and software required for volumetric analysis may not be easily accessible, potentially making its use impractical in routine clinical practice.…”
Section: Neuroradiological Characteristics and Correlates Of White Ma...mentioning
confidence: 99%
“…Although high performances were reported (88% Dice score), the database was composed by only 18 subjects, in which all tumours always grew. In Rachmadi et al (2020), they compared different GAN networks to predict the evolution of white matter hyperintensities. They also demonstrated the potential of using GANs in a semi-supervised scheme, improving results of a deterministic U-ResNet (Zhang et al, 2018).…”
Section: Deep Generative Networkmentioning
confidence: 99%