The reliability of different methods of manual volumetric segmentation of pharyngeal and sinonasal subregions

Neelapu, Bala Chakravarthy; Kharbanda, Om Prakash; Sardana, Harish Kumar; Gupta, Abhishek; Vasamsetti, Srikanth; Balachandran, Rajiv; Rana, Shailendra Singh; Sardana, Viren

doi:10.1016/j.oooo.2017.08.020

Cited by 13 publications

(5 citation statements)

References 37 publications

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“…The boundaries limits and segmentation method used for the airway parameters (Table 1) were selected based on the works of Neelapu BC. 20 The volume of the nasopharyngeal airway, oropharyngeal airway, hypopharyngeal airway, nasoethmoid cavity (nasal cavity and ethmoid sinus), right and left maxillary sinus and oral cavity airway were calculated. Segmentation of nasal cavity per se was not possible due to illdefined boundaries.…”

Section: Methodsmentioning

confidence: 99%

Influence of tongue volume, oral cavity volume and their ratio on upper airway: A cone beam computed tomography study

Rana

Kharbanda

Agarwal

2020

Journal of Oral Biology and Craniofacial Research

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Background: Our study aimed to measure the tongue volume (TV), oral cavity volume (OCV), and their ratio (TV/OCV) and correlate with upper airway using cone-beam computed tomography (CBCT). Method: The volume of oral cavity, tongue and upper airway were obtained by the manual process of segmentation of CBCT data of 15 subjects. The mean age of the sample was 21.86 years (range 15-33 years). Segmentation of the upper airway, tongue and oral cavity was performed manually using Mimics 11.0 (Materialise, Leuven, Belgium) software at different thresholds for air and the tongue. The Hounsfield units (HU) for airway volume of the different facial region ranged from −1024 to −500. For tongue volume, Hounsfield units (HU), ranging from −200 to 200 was calculated. Results: A significant negative correlation between TV/OCV and oropharynx (r = − 0.51; P = 0.04), TV/OCV and oral cavity airway volume (r = − 0.74; P = 0.002) was found. There was a significant and a positive correlation with TV/OCV and tongue volume (r = 0.65; P = 0.009). Conclusion: A significant negative correlation established between TV/OCV, oropharynx and oral cavity airway volume. This finding indicates an influence tongue volume, oral cavity volume and their ratio on patency of the oropharynx.

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Section: Methodsmentioning

confidence: 99%

Influence of tongue volume, oral cavity volume and their ratio on upper airway: A cone beam computed tomography study

Rana

Kharbanda

Agarwal

2020

Journal of Oral Biology and Craniofacial Research

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“…Nowadays, a few studies have explored and discussed the possibilities of applying AI techniques in upper airway analysis [ 26 , 27 , 28 , 29 , 30 ]. For example, Çağla et al [ 31 ] developed an automatic pharyngeal airway segmentation in CBCT images by using a CNN system; a dice ratio of 0.919 and a weighted IoU of 0.993 were achieved.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Models for Automatic Upper Airway Segmentation and Minimum Cross-Sectional Area Localisation in Two-Dimensional Images

Chu

Zhang

et al. 2023

Bioengineering

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Objective: To develop and validate convolutional neural network algorithms for automatic upper airway segmentation and minimum cross-sectional area (CSAmin) localisation in two-dimensional (2D) radiographic airway images. Materials and Methods: Two hundred and one 2D airway images acquired using cone-beam computed tomography (CBCT) scanning were randomly assigned to a test group (n = 161) to train artificial intelligence (AI) models and a validation group (n = 40) to evaluate the accuracy of AI processing. Four AI models, UNet18, UNet36, DeepLab50 and DeepLab101, were trained to automatically segment the upper airway 2D images in the test group. Precision, recall, Intersection over Union, the dice similarity coefficient and size difference were used to evaluate the performance of the AI-driven segmentation models. The CSAmin height in each image was manually determined using three-dimensional CBCT data. The nonlinear mathematical morphology technique was used to calculate the CSAmin level. Height errors were assessed to evaluate the CSAmin localisation accuracy in the validation group. The time consumed for airway segmentation and CSAmin localisation was compared between manual and AI processing methods. Results: The precision of all four segmentation models exceeded 90.0%. No significant differences were found in the accuracy of any AI models. The consistency of CSAmin localisation in specific segments between manual and AI processing was 0.944. AI processing was much more efficient than manual processing in terms of airway segmentation and CSAmin localisation. Conclusions: We successfully developed and validated a fully automatic AI-driven system for upper airway segmentation and CSAmin localisation using 2D radiographic airway images.

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“…After the diagnosis of the first Covid case in 2019, several Deep Learning and Machine Learning based approaches like ChexNet [26], ChestNet [31], have been proposed to detect Covid-19. Image segmentation [9,22] can also be used for identifying the location and severity of the Covid. Most of the AI-based approaches used for the Covid detection use the pre-trained model because the publicly available dataset for the research number is small in size.…”

Section: Related Workmentioning

confidence: 99%

An AI-enabled pre-trained model-based Covid detection model using chest X-ray images

Gupta

Kunhare

Pathik

et al. 2022

Multimed Tools Appl

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The year 2020 and 2021 was the witness of Covid 19 and it was the leading cause of death throughout the world during this time period. It has an impact on a large geographic area, particularly in countries with a large population. Due to the fact that this novel coronavirus has been detected in all countries around the world, the World Health Organization (WHO) has declared Covid-19 to be a pandemic. This novel coronavirus spread quickly from person to person through the saliva droplets and direct or indirect contact with an infected person. The tests carried out to detect the Covid-19 are time-consuming and the primary cause of rapid growth in Covid19 cases. Early detection of Covid patient can play a significant role in controlling the Covid chain by isolation the patient and proper treatment at the right time. Recent research on Covid-19 claim that Chest CT and X-ray images can be used as the preliminary screening for Covid-19 detection. This paper suggested an Artificial Intelligence (AI) based approach for detecting Covid-19 by using X-ray and CT scan images. Due to the availability of the small Covid dataset, we are using a pre-trained model. In this paper, four pre-trained models named VGGNet-19, ResNet50, InceptionResNetV2 and MobileNet are trained to classify the X-ray images into the Covid and Normal classes. A model is tuned in such a way that a smaller percentage of Covid cases will be classified as Normal cases by employing normalization and regularization techniques. The updated binary cross entropy loss (BCEL) function imposes a large penalty for classifying any Covid class to Normal class. The experimental results reveal that the proposed InceptionResNetV2 model outperforms the other pre-trained model with training, validation and test accuracy of 99.2%, 98% and 97% respectively.

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The reliability of different methods of manual volumetric segmentation of pharyngeal and sinonasal subregions

Cited by 13 publications

References 37 publications

Influence of tongue volume, oral cavity volume and their ratio on upper airway: A cone beam computed tomography study

Influence of tongue volume, oral cavity volume and their ratio on upper airway: A cone beam computed tomography study

Deep Learning Models for Automatic Upper Airway Segmentation and Minimum Cross-Sectional Area Localisation in Two-Dimensional Images

An AI-enabled pre-trained model-based Covid detection model using chest X-ray images

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