Large-scale parcellation of the ventricular system using convolutional neural networks

Atlason, Hans E.; Shao, Muhan; Robertsson, Vidar; Sigurðsson, Sigurður; Guðnason, Vilmundur; Prince, Jerry L.; Ellingsen, Lotta M.

doi:10.1117/12.2514590

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…A few studies that use deep learning approaches to parcellate the ventricular system (or parts thereof) have been reported in the literature, including ( Ghafoorian et al, 2018 ; Huo et al, 2019 ); and ( Atlason et al, 2019 ). ( Ghafoorian et al, 2018 ) utilized a 2D fully convolutional network based on the U-Net architecture to segment the left and right lateral ventricles using T1-w and FLAIR images.…”

Section: Discussionmentioning

confidence: 99%

“…Both ( Ghafoorian et al, 2018 ) and ( Huo et al, 2019 ) were not specially designed for and have not been tested on patients with enlarged ventricles. ( Atlason et al, 2019 ) developed a patch-based 3D U-Net CNN using T1-w and T2-w MRIs for labeling the brain ventricular system in patients with ventriculomegaly. The training labels were generated by an automated whole brain segmentation algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed network is also robust to white matter hyperintensities (WMH), which are often associated with NPH and located adjacent to the lateral ventricles. WMH can sometimes negatively affect the outcome of automatic segmentation algorithms ( Atlason et al, 2019 ). In our NPH testing data, 59 of 65 subjects have WMH close to the lateral ventricles and by visually examining the results, we found the proposed method provided accurate parcellation of the ventricular system in the presence of WMH.…”

Section: Discussionmentioning

confidence: 99%

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Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

Shao

Han

Carass

et al. 2019

NeuroImage: Clinical

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Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathogenesis of ventricular enlargement and elucidate novel patterns of ventriculomegaly that can be associated with different diseases. One such disease is normal pressure hydrocephalus (NPH), a chronic form of hydrocephalus in older adults that causes dementia. Automatic parcellation of the ventricular system into its sub-compartments in patients with ventriculomegaly is quite challenging due to the large variation of the ventricle shape and size. Conventional brain labeling methods are time-consuming and often fail to identify the boundaries of the enlarged ventricles. We propose a modified 3D U-Net method to perform accurate ventricular parcellation, even with grossly enlarged ventricles, from magnetic resonance images (MRIs). We validated our method on a data set of healthy controls as well as a cohort of 95 patients with NPH with mild to severe ventriculomegaly and compared with several state-of-the-art segmentation methods. On the healthy data set, the proposed network achieved mean Dice similarity coefficient (DSC) of 0.895 ± 0.03 for the ventricular system. On the NPH data set, we achieved mean DSC of 0.973 ± 0.02, which is significantly ( p < 0.005) higher than four state-of-the-art segmentation methods we compared with. Furthermore, the typical processing time on CPU-base implementation of the proposed method is 2 min, which is much lower than the several hours required by the other methods. Results indicate that our method provides: 1) highly robust parcellation of the ventricular system that is comparable in accuracy to state-of-the-art methods on healthy controls; 2) greater robustness and significantly more accurate results on cases of ventricular enlargement; and 3) a tool that enables computation of novel imaging biomarkers for dilated ventricular spaces that characterize the ventricular system.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

Shao

Han

Carass

et al. 2019

NeuroImage: Clinical

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“…Conventional whole brain segmentation methods include atlas based methods [13][14][15][16], such as multi-atlas segmentation methods that use deformable registration of multiple annotated atlas images to the subject at hand. A key challenge when using the multi-atlas segmentation approaches is that the size and location of WMHs varies greatly between subjects and hence, they cannot be accurately registered from one subject to another [17][18][19]. Also, multi-atlas segmentation methods often rely solely on T1-weighted (T1-w) images, which do not provide as good WMH lesion contrast as Fluid-Attenuated Inversion Recovery (FLAIR) images.…”

Section: Introductionmentioning

confidence: 99%

“…These methods have successfully been used for ventricle segmentation [22,30] and WMH segmentation [23][24][25][26] separately. They generate results in a fraction of the time of the conventional methods mentioned above [17], which is important when analysing big data sets and for use in clinical settings, and they can easily incorporate multi-contrast information for greater accuracy [26,30]. However, it would be beneficial if methods performed well using a variety of imaging contrasts in case of missing MRI sequences.…”

Section: Introductionmentioning

confidence: 99%

A joint ventricle and WMH segmentation from MRI for evaluation of healthy and pathological changes in the aging brain

et al. 2022

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Age-related changes in brain structure include atrophy of the brain parenchyma and white matter changes of presumed vascular origin. Enlargement of the ventricles may occur due to atrophy or impaired cerebrospinal fluid (CSF) circulation. The co-occurrence of these changes in neurodegenerative diseases and in aging brains often requires investigators to take both into account when studying the brain, however, automated segmentation of enlarged ventricles and white matter hyperintensities (WMHs) can be a challenging task. Here, we present a hybrid multi-atlas segmentation and convolutional autoencoder approach for joint ventricle parcellation and WMH segmentation from magnetic resonance images (MRIs). Our fully automated approach uses a convolutional autoencoder to generate a standardized image of grey matter, white matter, CSF, and WMHs, which, in conjunction with labels generated by a multi-atlas segmentation approach, is then fed into a convolutional neural network to parcellate the ventricular system. Hence, our approach does not depend on manually delineated training data for new data sets. The segmentation pipeline was validated on both healthy elderly subjects and subjects with normal pressure hydrocephalus using ground truth manual labels and compared with state-of-the-art segmentation methods. We then applied the method to a cohort of 2401 elderly brains to investigate associations of ventricle volume and WMH load with various demographics and clinical biomarkers, using a multiple regression model. Our results indicate that the ventricle volume and WMH load are both highly variable in a cohort of elderly subjects and there is an independent association between the two, which highlights the importance of taking both the possibility of enlarged ventricles and WMHs into account when studying the aging brain.

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Automated Ventricle Parcellation and Evan’s Ratio Computation in Pre- and Post-Surgical Ventriculomegaly

Wang,

Feng,

Xue

et al. 2023

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

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Large-scale parcellation of the ventricular system using convolutional neural networks

Cited by 4 publications

References 9 publications

Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

A joint ventricle and WMH segmentation from MRI for evaluation of healthy and pathological changes in the aging brain

Automated Ventricle Parcellation and Evan’s Ratio Computation in Pre- and Post-Surgical Ventriculomegaly

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