Novel Algorithm for Automated Optic Nerve Sheath Diameter Measurement Using a Clustering Approach

Rajajee, Venkatakrishna; Soroushmehr, S. M. Reza; Williamson, Craig A.; Najarian, Kayvan; Gryak, Jonathan; Awad, Abdelrahman B; Ward, Kevin R.; Tiba, Mohamad H.

doi:10.1093/milmed/usaa231

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…In this section, the performance of the proposed approach is compared with the other similar studies. The techniques employed in other studies for ICP include Deep learning [72], FWHM [73], Clustering [74], AUTONoMA [75], and labelfree 3D segmentation [76].…”

Section: Resultsmentioning

confidence: 99%

“…Overall, it can be concluded that the proposed approach is the best methodology to detect the nerve optic in comparison to the other method. Then, label-free, As it is clearly demonstrated, the Deep learning method [72] seems to perform better than the Clustering method [74] in detecting vertical objects (like Nerve Optic), whereas the proposed method is much better not only in detecting such areas but also can eliminate unrelated vertical objects more accurately. Also, the proposed approach, Label-Free 3D segmentation, and the deep learning methods gain promising results in detecting the optic nerve between two dark areas.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

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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“…Reviewing the Bland-Altman plot shows larger differences with smaller ONSD measurements, in our experience, larger ONSDs tend to have higher contrast between subarachnoid space and surrounding structures making them easier to measure which may explain this trend. 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.…”

Section: Discussionmentioning

confidence: 99%

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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“…In addition to the standardization of technique, automated ONSD measurement approaches have been proposed to remove the subjectivity and human error in determining the boundaries of the ONS [ 13 – 15 ]. In particular, Moore et al developed an automated algorithm that could correctly identify and measure ONSD from blind (no B-mode shown to the probe operator) scans of an ocular phantom [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of commercially available point-of-care ultrasound for automated optic nerve sheath measurement

Moore,

Osika,

Satterly

et al. 2023

Ultrasound J

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Background Measurement of the optic nerve sheath diameter (ONSD) via ultrasonography has been proposed as a non-invasive metric of intracranial pressure that may be employed during in-field patient triage. However, first responders are not typically trained to conduct sonographic exams and/or do not have access to an expensive ultrasound device. Therefore, for successful deployment of ONSD measurement in-field, we believe that first responders must have access to low-cost, portable ultrasound and be assisted by artificial intelligence (AI) systems that can automatically interpret the optic nerve sheath ultrasound scan. We examine the suitability of five commercially available, low-cost, portable ultrasound devices that can be combined with future artificial intelligence algorithms to reduce the training required for and cost of in-field optic nerve sheath diameter measurement. This paper is focused on the quality of the images generated by these low-cost probes. We report results of a clinician preference survey and compare with a lab analysis of three quantitative image quality metrics across devices. We also examine the suitability of the devices in a hypothetical far-forward deployment using operators unskilled in ultrasound, with the assumption of a future onboard AI video interpreter. Results We find statistically significant differences in clinician ranking of the devices in the following categories: “Image Quality”, “Ease of Acquisition”, “Software”, and “Overall ONSD”. We show differences in signal-to-noise ratio, generalized contrast-to-noise ratio, point-spread function across the devices. These differences in image quality result in a statistically significant difference in manual ONSD measurement. Finally, we show that sufficiently wide transducers can capture the optic nerve sheath during blind (no visible B-mode) scans performed by operators unskilled in sonography. Conclusions Ultrasound of the optic nerve sheath has the potential to be a convenient, non-invasive, point-of-injury or triage measure for elevated intracranial pressure in cases of traumatic brain injury. When transducer width is sufficient, briefly trained operators may obtain video sequences of the optic nerve sheath without guidance. This data suggest that unskilled operators are able to achieve the images needed for AI interpretation. However, we also show that image quality differences between ultrasound probes may influence manual ONSD measurements.

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Novel Algorithm for Automated Optic Nerve Sheath Diameter Measurement Using a Clustering Approach

Cited by 5 publications

References 25 publications

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

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

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

Evaluation of commercially available point-of-care ultrasound for automated optic nerve sheath measurement

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