Dilated Saliency U-Net for White Matter Hyperintensities Segmentation Using Irregularity Age Map

Jeong, Yunhee; Rachmadi, Muhammad Febrian; Valdés-Hernández, Maria del C.; Komura, Taku

doi:10.3389/fnagi.2019.00150

Cited by 12 publications

(5 citation statements)

References 31 publications

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“…When we trained the proposed model, the DSC value on the training set could easily reach above 0.9, but the DSC value on the test set was only 0.833. This could be explained by falling into overfitting, which reflects a defect of the deep learning model (Jeong, Rachmadi, Valdés Hernández, & Komura, 2019; Lawrence, Giles, & Tsoi, 1997). One of the reasons could be that the amount of the training data was too small (only 54 subjects).…”

Section: Discussionmentioning

confidence: 99%

White matter hyperintensities segmentation using an ensemble of neural networks

Zhao

Jiang

et al. 2021

Human Brain Mapping

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White matter hyperintensities (WMHs) represent the most common neuroimaging marker of cerebral small vessel disease (CSVD). The volume and location of WMHs are important clinical measures. We present a pipeline using deep fully convolutional network and ensemble models, combining U‐Net, SE‐Net, and multi‐scale features, to automatically segment WMHs and estimate their volumes and locations. We evaluated our method in two datasets: a clinical routine dataset comprising 60 patients (selected from Chinese National Stroke Registry, CNSR) and a research dataset composed of 60 patients (selected from MICCAI WMH Challenge, MWC). The performance of our pipeline was compared with four freely available methods: LGA, LPA, UBO detector, and U‐Net, in terms of a variety of metrics. Additionally, to access the model generalization ability, another research dataset comprising 40 patients (from Older Australian Twins Study and Sydney Memory and Aging Study, OSM), was selected and tested. The pipeline achieved the best performance in both research dataset and the clinical routine dataset with DSC being significantly higher than other methods (p < .001), reaching .833 and .783, respectively. The results of model generalization ability showed that the model trained on the research dataset (DSC = 0.736) performed higher than that trained on the clinical dataset (DSC = 0.622). Our method outperformed widely used pipelines in WMHs segmentation. This system could generate both image and text outputs for whole brain, lobar and anatomical automatic labeling WMHs. Additionally, software and models of our method are made publicly available at https://www.nitrc.org/projects/what_v1.

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Section: Discussionmentioning

confidence: 99%

White matter hyperintensities segmentation using an ensemble of neural networks

Zhao

Jiang

et al. 2021

Human Brain Mapping

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“…U-net models have also been leveraged in quantifying white matter hyperintensities as biomarkers for age-related neurologic disorders [62]. White matter changes have been shown to be involved in various forms of cortical dementia, such as AD, and manifest themselves as high-intensity regions in T2-fluid-attenuated inversion recovery (FLAIR) MRI scans [63].…”

Section: Medical Image Segmentationmentioning

confidence: 99%

“…In addition to quantifying PVSs, U-nets have been used in segmentation efforts to identify regions of abnormally intense white matter signals. In 2019, Jeong et al proposed a sailiency U-net, a U-net combined with simple regional maps, with the aim to lower the computational demand of the architecture while maintaining performance in order to identify areas of signal intensity in T2-FLAIR MRI scans of patients with AD [62,64]. Their model achieved a Dice coefficient score of 0.544 and a sensitivity of 0.459, indicating the utility of such a model to augment clinical image analysis [62].…”

Section: Medical Image Segmentationmentioning

confidence: 99%

“…In 2019, Jeong et al proposed a sailiency U-net, a U-net combined with simple regional maps, with the aim to lower the computational demand of the architecture while maintaining performance in order to identify areas of signal intensity in T2-FLAIR MRI scans of patients with AD [62,64]. Their model achieved a Dice coefficient score of 0.544 and a sensitivity of 0.459, indicating the utility of such a model to augment clinical image analysis [62]. The efforts described above in neuroanatomical segmentation and anomaly detection highlight the versatility of deep learning in analyzing an inherently complex organ system.…”

Section: Medical Image Segmentationmentioning

confidence: 99%

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Deep Learning and Neurology: A Systematic Review

2019

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Deciphering the massive volume of complex electronic data that has been compiled by hospital systems over the past decades has the potential to revolutionize modern medicine, as well as present significant challenges. Deep learning is uniquely suited to address these challenges, and recent advances in techniques and hardware have poised the field of medical machine learning for transformational growth. The clinical neurosciences are particularly well positioned to benefit from these advances given the subtle presentation of symptoms typical of neurologic disease. Here we review the various domains in which deep learning algorithms have already provided impetus for change-areas such as medical image analysis for the improved diagnosis of Alzheimer's disease and the early detection of acute neurologic events; medical image segmentation for quantitative evaluation of neuroanatomy and vasculature; connectome mapping for the diagnosis of Alzheimer's, autism spectrum disorder, and attention deficit hyperactivity disorder; and mining of microscopic electroencephalogram signals and granular genetic signatures. We additionally note important challenges in the integration of deep learning tools in the clinical setting and discuss the barriers to tackling the challenges that currently exist.

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“…The U-Net, was mainly developed for segmenting neuronal structures in electron microscopic stacks and light microscopic cell and tissue sections and works very effectively with comparatively little training data. In recent years, U-Net has been used successfully in medicine to segment certain structures and organs in chest x-rays and CT images ( Zhou et al, 2018 ; Alom et al, 2019 ; Dong et al, 2019 ; Jeong et al, 2019 ; Hojin et al, 2020 ; Seo et al, 2020b ; Umapathy et al, 2020 ; Causey et al, 2021 ; Ghosh et al, 2021 ; Wang Z. et al, 2021 ; Yan and Zhang, 2021 ). Some working groups have also segmented lungs in the CT images ( Skourt et al, 2018 ; Park et al, 2020 ; Zhou et al, 2020 ; Chen et al, 2021 ; Jalali et al, 2021 ; Kumar Singh et al, 2021 ; Qiblawey et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome

et al. 2021

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Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in acute respiratory distress syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a convolutional neural network (CNN) was used to train an artificial intelligence (AI) algorithm on 15 healthy subjects (1,302 slices), 100 ARDS patients (12,279 slices), and 20 COVID-19 (1,817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by intersection over union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5–10 s vs. 1–2 h of the manual. At slice level, the algorithm showed on the test set an IOU across all CT slices of 91.3 ± 10.0, 85.2 ± 13.9, and 84.7 ± 14.0%, and across all lung volumes of 96.3 ± 0.6, 88.9 ± 3.1, and 86.3 ± 6.5% for normal lungs, ARDS and COVID-19, respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R2 of 0.99 and a bias −9.8 ml [CI: +56.0/−75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI: +6.2/−5.5%] and −0.5% [CI: +2.3/−3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically.

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Dilated Saliency U-Net for White Matter Hyperintensities Segmentation Using Irregularity Age Map

Cited by 12 publications

References 31 publications

White matter hyperintensities segmentation using an ensemble of neural networks

White matter hyperintensities segmentation using an ensemble of neural networks

Deep Learning and Neurology: A Systematic Review

Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome

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