Measurement of Optic Nerve Sheath on Ocular Ultrasound Image Based on Segmentation by CNN

Pang, Maolin; Liu, Sunao; Lin, Fanchao; Liu, Songlin; Tian, Bei; Yang, Wenli; Chen, Xuejin

doi:10.1109/icsidp47821.2019.9173198

Cited by 4 publications

(9 citation statements)

References 17 publications

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“…Existing ONSD measurement methods almost exclusively utilize classical image processing, [24][25][26][27][28][29] with the use of ML being reported for ONSD automation in two studies. 22,30 Some classical image processing approaches are limited by utilizing phantoms 24,28 that lack the anatomic detail needed to obtain accurate ONSD measurements. 13,31 Other approaches apply image processing to ultrasound images obtained from humans.…”

Section: Discussionmentioning

confidence: 99%

“…29 Two studies utilize ML for automated ONSD measurement. 22,30 One study is limited by a lack of clear anatomic differentiation of the ONS, ONSD overestimation due to the inclusion of shadow artifacts, incorrect ON trajectory and retina estimation, small sample size, and not testing the CNN model on an independent sample. 30 A more recent work uses a larger sample size (201 highquality images from 50 patients).…”

Section: Discussionmentioning

confidence: 99%

“…22,30 One study is limited by a lack of clear anatomic differentiation of the ONS, ONSD overestimation due to the inclusion of shadow artifacts, incorrect ON trajectory and retina estimation, small sample size, and not testing the CNN model on an independent sample. 30 A more recent work uses a larger sample size (201 highquality images from 50 patients). However, agreement between CNN estimates and human experts was not examined and a relatively high MAE of 0.48 ± 0.48 mm is estimated.…”

Section: Discussionmentioning

confidence: 99%

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Automation of ultrasonographic optic nerve sheath diameter measurement using convolutional neural networks

Hirzallah,

Bose,

et al. 2023

Journal of Neuroimaging

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Background and purposeUltrasonographic optic nerve sheath (ONS) diameter is a noninvasive intracranial pressure (ICP) surrogate. ICP is monitored invasively in specialized intensive care units. Noninvasive ICP monitoring is important in less specialized settings. However, noninvasive ICP monitoring using ONS diameter (ONSD) is limited by the need for experts to obtain and perform measurements. We aim to automate ONSD measurements using a deep convolutional neural network (CNN) with a novel masking technique.MethodsWe trained a CNN to reproduce masks that mark the ONS. The edges of the mask are defined by an expert. Eight models were trained with 1000 epochs per model. The Dice‐similarity‐coefficient‐weighted averaged outputs of the eight models yielded the final predicted mask. Eight hundred and seventy‐three images were obtained from 52 transorbital cine‐ultrasonography sessions, performed on 46 patients with brain injuries. Eight hundred and fourteen images from 48 scanning sessions were used for training and validation and 59 images from four sessions for testing. Bland‐Altman and Pearson linear correlation analyses were used to evaluate the agreement between CNN and expert measurements.ResultsExpert ONSD measurements and CNN‐derived ONSD estimates had strong agreement (r = 0.7, p < .0001). The expert mean ONSD (standard deviation) is 5.27 mm (0.43) compared to CNN mean estimate of 5.46 mm (0.37). Mean difference (95% confidence interval, p value) is 0.19 mm (0.10‐0.27 mm, p = .0011), and root mean square error is 0.27 mm.ConclusionA CNN can learn ONSD measurement using masking without image segmentation or landmark detection.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Automation of ultrasonographic optic nerve sheath diameter measurement using convolutional neural networks

Hirzallah,

Bose,

et al. 2023

Journal of Neuroimaging

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“…Deep learning models have been proposed to advance in various medical imaging tasks, including improving our clinical understanding of the ON 17 . Automatic segmentation of the ON using deep learning has been explored in several studies, such as for studying glaucoma progression 18 , 19 or diagnosing increased intracranial pressure 20 , 21 . On MRI, deep learning techniques for automatic ON segmentation have primarily been investigated in the context of OARs segmentation for radiotherapy planning, where they have been shown to outperform atlas-based approaches 22 .…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation and quantification of the optic nerve on MRI using a 3D U-Net

van Elst,

de Bloeme,

Noteboom

et al. 2023

J. Med. Imag.

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Purpose: Pathological conditions associated with the optic nerve (ON) can cause structural changes in the nerve. Quantifying these changes could provide further understanding of disease mechanisms. We aim to develop a framework that automatically segments the ON separately from its surrounding cerebrospinal fluid (CSF) on magnetic resonance imaging (MRI) and quantifies the diameter and cross-sectional area along the entire length of the nerve.Approach: Multicenter data were obtained from retinoblastoma referral centers, providing a heterogeneous dataset of 40 high-resolution 3D T2-weighted MRI scans with manual ground truth delineations of both ONs. A 3D U-Net was used for ON segmentation, and performance was assessed in a tenfold cross-validation (n ¼ 32) and on a separate test-set (n ¼ 8) by measuring spatial, volumetric, and distance agreement with manual ground truths. Segmentations were used to quantify diameter and cross-sectional area along the length of the ON, using centerline extraction of tubular 3D surface models. Absolute agreement between automated and manual measurements was assessed by the intraclass correlation coefficient (ICC). Results:The segmentation network achieved high performance, with a mean Dice similarity coefficient score of 0.84, median Hausdorff distance of 0.64 mm, and ICC of 0.95 on the test-set. The quantification method obtained acceptable correspondence to manual reference measurements with mean ICC values of 0.76 for the diameter and 0.71 for the cross-sectional area. Compared with other methods, our method precisely identifies the ON from surrounding CSF and accurately estimates its diameter along the nerve's centerline. Conclusions:Our automated framework provides an objective method for ON assessment in vivo.

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“…The assessment of ONSD manually is time-consuming and subject to human errors. Consequently, a measurement approach based on automatic machine learning techniques can eliminate some potential errors [13][14][15]. Labeling similar regions in an input image are considered image segmentation and is a key step in the field of medical image analysis.…”

Section: Introductionmentioning

confidence: 99%

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Ranjbarzadeh

Dorosti

Ghoushchi

et al. 2022

Complex Intell. Syst.

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The increased intracranial pressure (ICP) can be described as an increase in pressure around the brain and can lead to serious health problems. The assessment of ultrasound images is commonly conducted by skilled experts which is a time-consuming approach, but advanced computer-aided diagnosis (CAD) systems can assist the physician to decrease the time of ICP diagnosis. The accurate detection of the nerve optic regions, with drawing a precise slope line behind the eyeball and calculating the diameter of nerve optic, are the main aims of this research. First, the Fuzzy C-mean (FCM) clustering is employed for segmenting the input CT screening images into the different parts. Second, a histogram equalization approach is used for region-based image quality enhancement. Then, the Local Directional Number method (LDN) is used for representing some key information in a new image. Finally, a cascade Convolutional Neural Network (CNN) is employed for nerve optic segmentation by two distinct input images. Comprehensive experiments on the CT screening dataset [The Cancer Imaging Archive (TCIA)] consisting of 1600 images show the competitive results of inaccurate extraction of the brain features. Also, the indexes such as Dice, Specificity, and Precision for the proposed approach are reported 87.7%, 91.3%, and 90.1%, respectively. The final classification results show that the proposed approach effectively and accurately detects the nerve optic and its diameter in comparison with the other methods. Therefore, this method can be used for early diagnose of ICP and preventing the occurrence of serious health problems in patients.

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Measurement of Optic Nerve Sheath on Ocular Ultrasound Image Based on Segmentation by CNN

Cited by 4 publications

References 17 publications

Automation of ultrasonographic optic nerve sheath diameter measurement using convolutional neural networks

Automation of ultrasonographic optic nerve sheath diameter measurement using convolutional neural networks

Automatic segmentation and quantification of the optic nerve on MRI using a 3D U-Net

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

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