Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

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COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

Diagnostics ◽

10.3390/diagnostics11081405 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1405

Author(s):

Jasjit S. Suri ◽

Sushant Agarwal ◽

Rajesh Pathak ◽

Vedmanvitha Ketireddy ◽

Marta Columbu ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Ground Truth ◽

P Value ◽

Image Analysis System ◽

Validation Data ◽

Lung Segmentation ◽

Intelligence Models ◽

Artificial Intelligence Models ◽

Analysis System

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

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Deep learning for COVID-19 chest CT (computed tomography) image analysis: a lesson from lung cancer

10.21203/rs.3.rs-58467/v1 ◽

2020 ◽

Author(s):

Hao Jiang ◽

Shiming Tang ◽

Weihuang Liu ◽

Yang Zhang

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Image Analysis ◽

Deep Learning ◽

Diagnostic Testing ◽

Ct Images ◽

Chest Ct ◽

Learning Models ◽

Tomography Image ◽

Computed Tomography Image

Abstract As a recent global health emergency, the quick and reliable diagnosis of COVID-19 is urgently needed. Thus, many artificial intelligence (AI)-base methods are proposed for COVID-19 chest CT (computed tomography) image analysis. However, there are very limited COVID-19 chest CT images publicly available to evaluate those deep neural networks. On the other hand, a huge amount of CT images from lung cancer are publicly available. To build a reliable deep learning model trained and tested with a larger scale dataset, we build a public COVID-19 CT dataset, containing 1186 CT images synthesized from lung cancer CT images using CycleGAN. Additionally, various deep learning models are tested with synthesized or real CT images for COVID-19 and non-COVID-19 classification. In comparison, all models achieve excellent results (over than 90%) in accuracy, precision, recall and F1 score for both synthesized and real COVID-19 CT images, demonstrating the reliable of the synthesized dataset. The public dataset and deep learning models can facilitate the development of accurate and efficient diagnostic testing for COVID-19.

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Abstract P319: Can Deep Learning Find the Ischemic Core on CT? Transfer Learning From Pre-Trained MRI-Based Networks

Stroke ◽

10.1161/str.52.suppl_1.p319 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Yannan Yu ◽

Soren Christensen ◽

Yuan Xie ◽

Enhao Gong ◽

Maarten G Lansberg ◽

...

Keyword(s):

Deep Learning ◽

Ground Truth ◽

Learning Model ◽

Fine Tuning ◽

Learning Models ◽

Starting Point ◽

Stroke Lesion ◽

Ischemic Core ◽

Deep Learning Model

Objective: Ischemic core prediction from CT perfusion (CTP) remains inaccurate compared with gold standard diffusion-weighted imaging (DWI). We evaluated if a deep learning model to predict the DWI lesion from MR perfusion (MRP) could facilitate ischemic core prediction on CTP. Method: Using the multi-center CRISP cohort of acute ischemic stroke patient with CTP before thrombectomy, we included patients with major reperfusion (TICI score≥2b), adequate image quality, and follow-up MRI at 3-7 days. Perfusion parameters including Tmax, mean transient time, cerebral blood flow (CBF), and cerebral blood volume were reconstructed by RAPID software. Core lab experts outlined the stroke lesion on the follow-up MRI. A previously trained MRI model in a separate group of patients was used as a starting point, which used MRP parameters as input and RAPID ischemic core on DWI as ground truth. We fine-tuned this model, using CTP parameters as input, and follow-up MRI as ground truth. Another model was also trained from scratch with only CTP data. 5-fold cross validation was used. Performance of the models was compared with ischemic core (rCBF≤30%) from RAPID software to identify the presence of a large infarct (volume>70 or >100ml). Results: 94 patients in the CRISP trial met the inclusion criteria (mean age 67±15 years, 52% male, median baseline NIHSS 18, median 90-day mRS 2). Without fine-tuning, the MRI model had an agreement of 73% in infarct >70ml, and 69% in >100ml; the MRI model fine-tuned on CT improved the agreement to 77% and 73%; The CT model trained from scratch had agreements of 73% and 71%; All of the deep learning models outperformed the rCBF segmentation from RAPID, which had agreements of 51% and 64%. See Table and figure. Conclusions: It is feasible to apply MRP-based deep learning model to CT. Fine-tuning with CTP data further improves the predictions. All deep learning models predict the stroke lesion after major recanalization better than thresholding approaches based on rCBF.

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Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging

Insights into Imaging ◽

10.1186/s13244-021-01105-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Yazdan Salimi ◽

Isaac Shiri ◽

Azadeh Akhavanallaf ◽

Zahra Mansouri ◽

Abdollah Saberi Manesh ◽

...

Keyword(s):

Deep Learning ◽

Radiation Dose ◽

Absorbed Dose ◽

Test Group ◽

Ground Truth ◽

Chest Ct ◽

Fine Tuning ◽

Reduce Radiation Dose ◽

Automatic Methods ◽

Range Selection

Abstract Background Despite the prevalence of chest CT in the clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed to assess the additional radiation dose associated with overscanning in chest CT and to develop an automated deep learning-assisted scan range selection technique to reduce radiation dose to patients. Results A significant overscanning range (31 ± 24) mm was observed in clinical setting for over 95% of the cases. The average Dice coefficient for lung segmentation was 0.96 and 0.97 for anterior–posterior (AP) and lateral projections, respectively. By considering the exact lung coverage as the ground truth, and AP and lateral projections as input, The DL-based approach resulted in errors of 0.08 ± 1.46 and − 1.5 ± 4.1 mm in superior and inferior directions, respectively. In contrast, the error on external scout views was − 0.7 ± 4.08 and 0.01 ± 14.97 mm for superior and inferior directions, respectively.The ED reduction achieved by automated scan range selection was 21% in the test group. The evaluation of a large multi-centric chest CT dataset revealed unnecessary ED of more than 2 mSv per scan and 67% increase in the thyroid absorbed dose. Conclusion The proposed DL-based solution outperformed previous automatic methods with acceptable accuracy, even in complicated and challenging cases. The generizability of the model was demonstrated by fine-tuning the model on AP scout views and achieving acceptable results. The method can reduce the unoptimized dose to patients by exclunding unnecessary organs from field of view.

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Metrics for Automatic Evaluation of Text from NLP Models for Text to Scene Generation

European Journal of Electrical Engineering and Computer Science ◽

10.24018/ejece.2021.5.4.341 ◽

2021 ◽

Vol 5 (4) ◽

pp. 20-25

Author(s):

S. Yashaswini ◽

S. S. Shylaja

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Cost Function ◽

Performance Metrics ◽

Evaluation Criteria ◽

Ground Truth ◽

Automatic Evaluation ◽

Mean Square ◽

Learning Models ◽

Classification Tasks

Performance metrics give us an indication of which model is better for which task. Researchers attempt to apply machine learning and deep learning models to measure the performance of models through cost function or evaluation criteria like Mean square error (MSE) for regression, accuracy, and f1-score for classification tasks Whereas in NLP performance measurement is a complex due variation of ground truth and results obta.

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Diagnosis of COVID-19 using CT scan images and deep learning techniques

10.1101/2020.07.11.20151332 ◽

2020 ◽

Author(s):

Vruddhi Shah ◽

Rinkal Keniya ◽

Akanksha Shridharani ◽

Manav Punjabi ◽

Jainam Shah ◽

...

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Deep Learning ◽

Early Diagnosis ◽

Ct Scan ◽

Efficient Method ◽

Learning Models ◽

Automated Diagnosis ◽

Learning Techniques ◽

The World

Early diagnosis of the coronavirus disease in 2019 (COVID-19) is essential for controlling this pandemic. COVID-19 has been spreading rapidly all over the world. There is no vaccine available for this virus yet. Fast and accurate COVID-19 screening is possible using computed tomography (CT) scan images. The deep learning techniques used in the proposed method was based on a convolutional neural network (CNN). Our manuscript focuses on differentiating the CT scan images of COVID-19 and non-COVID 19 CT using different deep learning techniques. A self developed model named CTnet-10 was designed for the COVID-19 diagnosis, having an accuracy of 82.1 %. Also, other models that we tested are DenseNet-169, VGG-16, ResNet-50, InceptionV3, and VGG-19. The VGG-19 proved to be superior with an accuracy of 94.52 % as compared to all other deep learning models. Automated diagnosis of COVID-19 from the CT scan pictures can be used by the doctors as a quick and efficient method for COVID-19 screening.

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Automatic Detection of the Aortic Annular Plane and Coronary Ostia from Multidetector Computed Tomography

Journal of Interventional Cardiology ◽

10.1155/2020/9843275 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Patricio Astudillo ◽

Peter Mortier ◽

Johan Bosmans ◽

Ole De Backer ◽

Peter de Jaegere ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Aortic Valve ◽

Multidetector Computed Tomography ◽

Preoperative Planning ◽

Ground Truth ◽

Anatomical Landmarks ◽

Landmark Detection ◽

Mdct Image ◽

Coronary Ostia

Anatomic landmark detection is crucial during preoperative planning of transcatheter aortic valve implantation (TAVI) to select the proper device size and assess the risk of complications. The detection is currently a time-consuming manual process influenced by the image quality and subject to operator variability. In this work, we propose a novel automatic method to detect the relevant aortic landmarks from MDCT images using deep learning techniques. We trained three convolutional neural networks (CNNs) with 344 multidetector computed tomography (MDCT) acquisitions to detect five anatomical landmarks relevant for TAVI planning: the three basal attachment points of the aortic valve leaflets and the left and right coronary ostia. The detection strategy used these three CNN models to analyse a single MDCT image and yield three segmentation volumes as output. These segmentation volumes were averaged into one final segmentation volume, and the final predicted landmarks were obtained during a postprocessing step. Finally, we constructed the aortic annular plane, defined by the three predicted hinge points, and measured the distances from this plane to the predicted coronary ostia (i.e., coronary height). The methodology was validated on 100 patients. The automatic landmark detection was able to detect all the landmarks and showed high accuracy as the median distance between the ground truth and predictions is lower than the interobserver variations (1.5 mm [1.1–2.1], 2.0 mm [1.3–2.8] with a paired difference −0.5 ± 1.3 mm and p value <0.001). Furthermore, a high correlation is observed between predicted and manually measured coronary heights (for both R2 = 0.8). The image analysis time per patient was below one second. The proposed method is accurate, fast, and reproducible. Embedding this tool based on deep learning in the preoperative planning routine may have an impact in the TAVI environments by reducing the time and cost and improving accuracy.

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Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/9756518 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Ilker Ozsahin ◽

Boran Sekeroglu ◽

Musa Sani Musa ◽

Mubarak Taiwo Mustapha ◽

Dilber Uzun Ozsahin

Keyword(s):

Artificial Intelligence ◽

Computed Tomography ◽

Neural Networks ◽

Deep Learning ◽

Chest Radiography ◽

Area Under The Curve ◽

Chest Ct ◽

Chest Computed Tomography ◽

Classification Tasks ◽

Sensitivity Specificity

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms “deep learning”, “neural networks”, “COVID-19”, and “chest CT”. At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

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Lung Segmentation and Iterative Weighted Averaging Smoothing Technique on Chest Ct Images

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1084.1291s319 ◽

2019 ◽

Vol 9 (1S3) ◽

pp. 383-387

Keyword(s):

Computed Tomography ◽

Anisotropic Diffusion ◽

Clustering Algorithm ◽

Chest Ct ◽

Weighted Averaging ◽

Lung Tumour ◽

Lung Segmentation ◽

Averaging Technique ◽

Diffusion Filtering ◽

Nonlinear Anisotropic Diffusion

Computed Tomography (CT) is one of the most commonly used imaging modalities for tumour detection and diagnosis, due to its high spatial resolution, fast imaging speed and wide availability. Nodules of the lungs and pathological residues with varied diameter can be comfortably viewed by computed tomography and can be categorized as benign or malignant. The key intention of this segmentation and smoothing is to develop an efficient methodology for an automated lung tumour diagnosis. Segmentation is the quantitative preprocessing step used in the chest CT analysis. The iterative weighted averaging technique is used in addressing the issues related to the segmentation and smoothing method employed in this paper. The methodology incorporates the accurate Lung Segmentation, enclosure of Juxtapleural nodules, the proper insertion of the bronchial vessels for enhancing the smoothness of the segmented Lung area. The steps involved in this paper include Image preprocessing by nonlinear anisotropic diffusion filtering, Thorax Extraction, Lung segmentation and iterative weighted averaging technique for smoothing the contours. The main feature in choosing the nonlinear anisotropic diffusion filtering is for properly preserving the regions unaffected with cancer and also to differentiate the artefacts involved due to the subject movements. In the fuzzy c- means clustering algorithm, the lung parenchyma is identified from the thorax region based on the fuzzy membership value and the affected regions are attached. The standard execution time for segmentation process of one dataset is 1.91s, it is the more rapid process than the manual segmentation.

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