Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks

Rachmadi, Muhammad Febrian; Valdés-Hernández, Maria del C.; Makin, Stephen; Wardlaw, Joanna M.; Komura, Taku

doi:10.1016/j.media.2020.101712

Cited by 18 publications

(23 citation statements)

References 51 publications

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“…Uncertainties are unavoidable when predicting the progression of WMHs, and a previous study showed that incorporating uncertainties into a deep learning model produced the best prediction results [8]. However, the models evaluated in [8] only incorporate external uncertainties (i.e., non-image factors of stroke lesions' volume and unrelated Gaussian noise) and not primary/secondary information coming from brain MRI scans (e.g. statistical spatial maps showing the association of specific WMHs voxels with clinical variables like smoking status).…”

Section: Probabilistic Model For Capturing Spatial Uncertaintymentioning

confidence: 99%

“…Previous studies have proposed various unsupervised and supervised deep learning models to predict the progression (i.e., evolution) of WMHs [8,9]. In the supervised approaches, a deep learning model learns to perform multi-class segmentation of non-WMHs, shrinking WMHs, growing WMHs, and stable WMHs from the namely disease evolution map (DEM).…”

Section: Introductionmentioning

confidence: 99%

“…One study [8] exposed two big challenges in predicting the progression of WMHs: 1) spatial uncertainty in predicting regions of WMHs dynamic changes and their boundaries (i.e., voxels of growth and shrinkage), and 2) uncertainty in the estimation of future WMHs volume (i.e., closeness between the predicted volume of WMHs and the true future volume of WMHs). In relation to the first challenge, it was observed that it is difficult to distinguish the intensities/textures of shrinking and growing WMHs in the MRI sequence used by the study (i.e., T2-FLAIR).…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution

Rachmadi

Valdés-Hernández

Maulana

et al. 2021

Predictive Intelligence in Medicine

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Predicting disease progression always involves a high degree of uncertainty. White matter hyperintensities (WMHs) are the main neuroradiological feature of small vessel disease and a common finding in brain scans of dementia patients and older adults. In predicting their progression previous studies have identified two main challenges: 1) uncertainty in predicting the areas/boundaries of shrinking and growing WMHs and 2) uncertainty in the estimation of future WMHs volume. This study proposes the use of a probabilistic deep learning model called Probabilistic U-Net trained with adversarial loss for capturing and modelling spatial uncertainty in brain MR images. This study also proposes an evaluation procedure named volume interval estimation (VIE) for improving the interpretation of and confidence in the predictive deep learning model. Our experiments show that the Probabilistic U-Net with adversarial training improved the performance of non-probabilistic U-Net in Dice similarity coefficient for predicting the areas of shrinking WMHs, growing WMHs, stable WMHs, and their average by up to 3.35%, 2.94%, 0.47%, and 1.03% respectively. It also improved the volume estimation by 11.84% in the "Correct Prediction in Estimated Volume Interval" metric as per the newly proposed VIE evaluation procedure.

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Section: Probabilistic Model For Capturing Spatial Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution

Rachmadi

Valdés-Hernández

Maulana

et al. 2021

Predictive Intelligence in Medicine

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“…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 Matter Lesionsmentioning

confidence: 99%

Emerging role of white matter lesions in cerebrovascular disease

Rastogi

Weissert

Bhaskar

2021

Eur J of Neuroscience

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White matter lesions have been implicated in the setting of stroke, dementia, intracerebral haemorrhage, several other cerebrovascular conditions, migraine, various neuroimmunological diseases like multiple sclerosis, disorders of metabolism, mitochondrial diseases and others. While much is understood vis a vis neuroimmunological conditions, our knowledge of the pathophysiology of these lesions, and their role in, and implications to, management of cerebrovascular diseases or stroke, especially in the elderly, are limited. Several clinical assessment tools are available for delineating white matter lesions in clinical practice. However, their incorporation into clinical decision‐making and specifically prognosis and management of patients is suboptimal for use in standards of care. This article sought to provide an overview of the current knowledge and recent advances on pathophysiology, as well as clinical and radiological assessment, of white matter lesions with a focus on its development, progression and clinical implications in cerebrovascular diseases. Key indications for clinical practice and recommendations on future areas of research are also discussed. Finally, a conceptual proposal on putative mechanisms underlying pathogenesis of white matter lesions in cerebrovascular disease has been presented. Understanding of pathophysiology of white matter lesions and how they mediate outcomes is important to develop therapeutic strategies.

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“…Rachmadi et al [95] proposed a disease evolution predictor (DEP) model to predict the evolution of white matter hyperintensities from baseline to follow-up (i.e., one year later). The DEP model takes a baseline image as input to generate a disease evolution map representing the evolution of the disease.…”

Section: Predictionmentioning

confidence: 99%

When medical images meet generative adversarial network: recent development and research opportunities

Jiang

Rodríguez-Andina

et al. 2021

Discov Artif Intell

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Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed well in computer vision. Medical image analysis is an important application of deep learning, which is expected to greatly reduce the workload of doctors, contributing to more sustainable health systems. However, most current AI methods for medical image analysis are based on supervised learning, which requires a lot of annotated data. The number of medical images available is usually small and the acquisition of medical image annotations is an expensive process. Generative adversarial network (GAN), an unsupervised method that has become very popular in recent years, can simulate the distribution of real data and reconstruct approximate real data. GAN opens some exciting new ways for medical image generation, expanding the number of medical images available for deep learning methods. Generated data can solve the problem of insufficient data or imbalanced data categories. Adversarial training is another contribution of GAN to medical imaging that has been applied to many tasks, such as classification, segmentation, or detection. This paper investigates the research status of GAN in medical images and analyzes several GAN methods commonly applied in this area. The study addresses GAN application for both medical image synthesis and adversarial learning for other medical image tasks. The open challenges and future research directions are also discussed.

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Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks

Cited by 18 publications

References 51 publications

Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution

Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution

Emerging role of white matter lesions in cerebrovascular disease

When medical images meet generative adversarial network: recent development and research opportunities

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