Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network

AbstractObjectiveConvolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.MethodConsecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.ResultsThe trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.ConclusionA trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.

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An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans

The Journal of Laryngology & Otology ◽

10.1017/s0022215119002536 ◽

2019 ◽

Vol 134 (1) ◽

pp. 52-55 ◽

Cited By ~ 3

Author(s):

J Huang ◽

A-R Habib ◽

D Mendis ◽

J Chong ◽

M Smith ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Computed Tomography ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Sinus Surgery ◽

Computed Tomography Scans ◽

Ethmoidal Artery ◽

Anterior Ethmoidal Artery

AbstractObjectiveDeep learning using convolutional neural networks represents a form of artificial intelligence where computers recognise patterns and make predictions based upon provided datasets. This study aimed to determine if a convolutional neural network could be trained to differentiate the location of the anterior ethmoidal artery as either adhered to the skull base or within a bone ‘mesentery’ on sinus computed tomography scans.MethodsCoronal sinus computed tomography scans were reviewed by two otolaryngology residents for anterior ethmoidal artery location and used as data for the Google Inception-V3 convolutional neural network base. The classification layer of Inception-V3 was retrained in Python (programming language software) using a transfer learning method to interpret the computed tomography images.ResultsA total of 675 images from 388 patients were used to train the convolutional neural network. A further 197 unique images were used to test the algorithm; this yielded a total accuracy of 82.7 per cent (95 per cent confidence interval = 77.7–87.8), kappa statistic of 0.62 and area under the curve of 0.86.ConclusionConvolutional neural networks demonstrate promise in identifying clinically important structures in functional endoscopic sinus surgery, such as anterior ethmoidal artery location on pre-operative sinus computed tomography.

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207 QUANTIFICATION OF PANCREATIC FAT ON ABDOMINAL COMPUTED TOMOGRAPHY SCANS USING A DEEP-CONVOLUTIONAL NEURAL NETWORK TRAINED FOR PANCREAS SEGMENTATION

Gastroenterology ◽

10.1016/s0016-5085(20)30780-0 ◽

2020 ◽

Vol 158 (6) ◽

pp. S-39

Author(s):

Laurens Janssens ◽

Alexander D. Weston ◽

William S. Harmsen ◽

Naoki Takahashi ◽

Kenneth A. Philbrick ◽

...

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Convolutional Neural Network ◽

Deep Convolutional Neural Network ◽

Abdominal Computed Tomography ◽

Pancreatic Fat ◽

Computed Tomography Scans ◽

Pancreas Segmentation

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A Convolutional Neural Network Approach to Automated Lung Bounding Box Estimation from Computed Tomography Scans

2019 IEEE Data Science Workshop (DSW) ◽

10.1109/dsw.2019.8755594 ◽

2019 ◽

Author(s):

Charles R. Hatt ◽

Sundaresh Ram ◽

Craig J. Galban

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Convolutional Neural Network ◽

Network Approach ◽

Neural Network Approach ◽

Bounding Box ◽

Computed Tomography Scans

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Convolutional neural network algorithms for computed tomography scans of chronic rhinosinusitis subtypes

International Forum of Allergy & Rhinology ◽

10.1002/alr.22742 ◽

2020 ◽

Author(s):

David A. Gudis

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Convolutional Neural Network ◽

Chronic Rhinosinusitis ◽

Network Algorithms ◽

Computed Tomography Scans

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Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi.2015.7163869 ◽

2015 ◽

Cited By ~ 101

Author(s):

Bram van Ginneken ◽

Arnaud A. A. Setio ◽

Colin Jacobs ◽

Francesco Ciompi

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Convolutional Neural Network ◽

Pulmonary Nodule ◽

Nodule Detection ◽

Computed Tomography Scans ◽

Pulmonary Nodule Detection

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Carpal Bone Segmentation Using Fully Convolutional Neural Network

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190724101600 ◽

2019 ◽

Vol 15 (10) ◽

pp. 983-989

Author(s):

Liang Kim Meng ◽

Azira Khalil ◽

Muhamad Hanif Ahmad Nizar ◽

Maryam Kamarun Nisham ◽

Belinda Pingguan-Murphy ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Automatic Segmentation ◽

Ground Truth ◽

Bone Age ◽

Image Feature ◽

Qualitative Assessment ◽

Carpal Bone ◽

Radiographic Images ◽

Accurate Quantitative Analysis

Background: Bone Age Assessment (BAA) refers to a clinical procedure that aims to identify a discrepancy between biological and chronological age of an individual by assessing the bone age growth. Currently, there are two main methods of executing BAA which are known as Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability accuracy and time-consuming. An automatic segmentation can be applied to the radiographs, providing the physician with more accurate delineation of the carpal bone and accurate quantitative analysis. Methods: In this study, we proposed an image feature extraction technique based on image segmentation with the fully convolutional neural network with eight stride pixel (FCN-8). A total of 290 radiographic images including both female and the male subject of age ranging from 0 to 18 were manually segmented and trained using FCN-8. Results and Conclusion: The results exhibit a high training accuracy value of 99.68% and a loss rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78 ± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall qualitative carpal recognition accuracy, respectively.

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Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis

Cancers ◽

10.3390/cancers13040652 ◽

2021 ◽

Vol 13 (4) ◽

pp. 652 ◽

Cited By ~ 2

Author(s):

Carlo Augusto Mallio ◽

Andrea Napolitano ◽

Gennaro Castiello ◽

Francesco Maria Giordano ◽

Pasquale D'Alessio ◽

...

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Deep Learning ◽

Convolutional Neural Network ◽

Immune Checkpoint ◽

Immune Checkpoint Inhibitor ◽

Learning Algorithm ◽

Checkpoint Inhibitor ◽

Deep Convolutional Neural Network ◽

Deep Learning Algorithm

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

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