Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume

Huff, Trevor; Ludwig, Parker E.; Salazar, David; Cramer, Justin A.

doi:10.1007/s11548-019-02038-5

Cited by 20 publications

(21 citation statements)

References 22 publications

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“…The fourth ventricle was excluded from the quantification of the ventricular system in a few studies (9,18). However, the fourth ventricle is also enlarged in extraventricular hydrocephalus, and an isolated fourth ventricle enlargement is a rare complication of neurosurgical treatment.…”

Section: Discussionmentioning

confidence: 99%

“…However, the inclusion of cerebral cisterns adjacent to the ventricles, such as the basal cistern, was unavoidable, and this resulted in an overestimation of ventricular volume (3). A few recent studies demonstrated that automated intracranial ventricle segmentation software using deep learning methods could reduce processing time (e.g., to less than 1 minute) with high accuracy (18,19). However, these automatic tools produced segmentation errors and remained at a pre-clinical stage of research (18,19).…”

Section: Discussionmentioning

confidence: 99%

“…A few recent studies demonstrated that automated intracranial ventricle segmentation software using deep learning methods could reduce processing time (e.g., to less than 1 minute) with high accuracy (18,19). However, these automatic tools produced segmentation errors and remained at a pre-clinical stage of research (18,19). The accuracy of deep learning methods depends on the quality and the number of ground truth data used for training.…”

Section: Discussionmentioning

confidence: 99%

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Hydrocephalus: Ventricular Volume Quantification Using Three-Dimensional Brain CT Data and Semiautomatic Three-Dimensional Threshold-Based Segmentation Approach

Goo

2021

Korean J Radiol

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Objective To evaluate the usefulness of the ventricular volume percentage quantified using three-dimensional (3D) brain computed tomography (CT) data for interpreting serial changes in hydrocephalus. Materials and Methods Intracranial and ventricular volumes were quantified using the semiautomatic 3D threshold-based segmentation approach for 113 brain CT examinations (age at brain CT examination ≤ 18 years) in 38 patients with hydrocephalus. Changes in ventricular volume percentage were calculated using 75 serial brain CT pairs (time interval 173.6 ± 234.9 days) and compared with the conventional assessment of changes in hydrocephalus (increased, unchanged, or decreased). A cut-off value for the diagnosis of no change in hydrocephalus was calculated using receiver operating characteristic curve analysis. The reproducibility of the volumetric measurements was assessed using the intraclass correlation coefficient on a subset of 20 brain CT examinations. Results Mean intracranial volume, ventricular volume, and ventricular volume percentage were 1284.6 ± 297.1 cm 3 , 249.0 ± 150.8 cm 3 , and 19.9 ± 12.8%, respectively. The volumetric measurements were highly reproducible (intraclass correlation coefficient = 1.0). Serial changes (0.8 ± 0.6%) in ventricular volume percentage in the unchanged group (n = 28) were significantly smaller than those in the increased and decreased groups (6.8 ± 4.3% and 5.6 ± 4.2%, respectively; p = 0.001 and p < 0.001, respectively; n = 11 and n = 36, respectively). The ventricular volume percentage was an excellent parameter for evaluating the degree of hydrocephalus (area under the receiver operating characteristic curve = 0.975; 95% confidence interval, 0.948–1.000; p < 0.001). With a cut-off value of 2.4%, the diagnosis of unchanged hydrocephalus could be made with 83.0% sensitivity and 100.0% specificity. Conclusion The ventricular volume percentage quantified using 3D brain CT data is useful for interpreting serial changes in hydrocephalus.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Hydrocephalus: Ventricular Volume Quantification Using Three-Dimensional Brain CT Data and Semiautomatic Three-Dimensional Threshold-Based Segmentation Approach

Goo

2021

Korean J Radiol

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“…Volumetric measurement is the only method to directly determine the ventricular size. It is realized by segmentation, which can be roughly categorized into automated segmentation and manual segmentation (Huff et al, 2019). The manual segmentation technique is the gold standard for volumetric quantification of regional brain structures (Kocaman et al, 2019), but when dealing with more data, manual segmentation of the ventricles is time-consuming, subjective, and less reproducible (Chou et al, 2008;Liu et al, 2009;Poh et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Some cerebellar ventricle areas (anterior, posterior, and inferior horns of the lateral ventricle) may not be recognized because they are not connected to the core of the lateral ventricle (Liu et al, 2010). In some automated ventricle segmentation methods, pathological ventricles were not included (Huff et al, 2019), but pathological ventricles are common in the elderly, especially in patients with acquired hydrocephalus, because they may have brain trauma, brain tumors, subarachnoid hemorrhage, and it becomes extremely difficult to delineate the ventricle from these patients. Previous literature also reported the segmentation of the ventricle of idiopathic Normal Pressure Hydrocephalus (iNPH) patients (Shao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Systematic and Comprehensive Automated Ventricle Segmentation on Ventricle Images of the Elderly Patients: A Retrospective Study

Zhou

Ye²,

Jiang³

et al. 2020

Front. Aging Neurosci.

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Background and Objective: Ventricle volume is closely related to hydrocephalus, brain atrophy, Alzheimer's, Parkinson's syndrome, and other diseases. To accurately measure the volume of the ventricles for elderly patients, we use deep learning to establish a systematic and comprehensive automated ventricle segmentation framework.Methods: The study participation included 20 normal elderly people, 20 patients with cerebral atrophy, 64 patients with normal pressure hydrocephalus, and 51 patients with acquired hydrocephalus. Second, get their imaging data through the picture archiving and communication systems (PACS) system. Then use ITK software to manually label participants' ventricular structures. Finally, extract imaging features through machine learning.Results: This automated ventricle segmentation method can be applied not only to CT and MRI images but also to images with different scan slice thicknesses. More importantly, it produces excellent segmentation results (Dice > 0.9).Conclusion: This automated ventricle segmentation method has wide applicability and clinical practicability. It can help clinicians find early disease, diagnose disease, understand the patient's disease progression, and evaluate the patient's treatment effect.

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Deep Learning Applications for Acute Stroke Management

Chavva

Crawford

Mazurek

et al. 2022

Annals of Neurology

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Brain imaging is essential to the clinical care of patients with stroke, a leading cause of disability and death worldwide. Whereas advanced neuroimaging techniques offer opportunities for aiding acute stroke management, several factors, including time delays, inter‐clinician variability, and lack of systemic conglomeration of clinical information, hinder their maximal utility. Recent advances in deep machine learning (DL) offer new strategies for harnessing computational medical image analysis to inform decision making in acute stroke. We examine the current state of the field for DL models in stroke triage. First, we provide a brief, clinical practice‐focused primer on DL. Next, we examine real‐world examples of DL applications in pixel‐wise labeling, volumetric lesion segmentation, stroke detection, and prediction of tissue fate postintervention. We evaluate recent deployments of deep neural networks and their ability to automatically select relevant clinical features for acute decision making, reduce inter‐rater variability, and boost reliability in rapid neuroimaging assessments, and integrate neuroimaging with electronic medical record (EMR) data in order to support clinicians in routine and triage stroke management. Ultimately, we aim to provide a framework for critically evaluating existing automated approaches, thus equipping clinicians with the ability to understand and potentially apply DL approaches in order to address challenges in clinical practice. ANN NEUROL 2022;92:574–587

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Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume

Cited by 20 publications

References 22 publications

Hydrocephalus: Ventricular Volume Quantification Using Three-Dimensional Brain CT Data and Semiautomatic Three-Dimensional Threshold-Based Segmentation Approach

Hydrocephalus: Ventricular Volume Quantification Using Three-Dimensional Brain CT Data and Semiautomatic Three-Dimensional Threshold-Based Segmentation Approach

Systematic and Comprehensive Automated Ventricle Segmentation on Ventricle Images of the Elderly Patients: A Retrospective Study

Deep Learning Applications for Acute Stroke Management

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