P2P-COVID-GAN

2021 ◽  
Vol 17 (4) ◽  
pp. 101-118
Author(s):  
Nandhini Abirami ◽  
Durai Raj Vincent ◽  
Seifedine Kadry
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Dice Coefficient ◽  
Computed Tomography Images ◽  
Proposed Model ◽  
Novel Method ◽  
Lung Infections ◽  
Lung Ct ◽  
Ct Slices

Early and automatic segmentation of lung infections from computed tomography images of COVID-19 patients is crucial for timely quarantine and effective treatment. However, automating the segmentation of lung infection from CT slices is challenging due to a lack of contrast between the normal and infected tissues. A CNN and GAN-based framework are presented to classify and then segment the lung infections automatically from COVID-19 lung CT slices. In this work, the authors propose a novel method named P2P-COVID-SEG to automatically classify COVID-19 and normal CT images and then segment COVID-19 lung infections from CT images using GAN. The proposed model outperformed the existing classification models with an accuracy of 98.10%. The segmentation results outperformed existing methods and achieved infection segmentation with accurate boundaries. The Dice coefficient achieved using GAN segmentation is 81.11%. The segmentation results demonstrate that the proposed model outperforms the existing models and achieves state-of-the-art performance.

scholarly journals ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans

2021 ◽  
Vol 7 ◽  
pp. e349
Author(s):  
Alex Noel Joseph Raj ◽  
Haipeng Zhu ◽  
Asiya Khan ◽  
Zhemin Zhuang ◽  
Zengbiao Yang ◽  
...  
Keyword(s):  
Pulmonary Infection ◽  
State Of The Art ◽  
Lung Infection ◽  
Ct Scans ◽  
Dense Network ◽  
Dice Coefficient ◽  
Proposed Model ◽  
Edge Features ◽  
Lung Ct ◽  
Data Acquisition Process

Currently, the new coronavirus disease (COVID-19) is one of the biggest health crises threatening the world. Automatic detection from computed tomography (CT) scans is a classic method to detect lung infection, but it faces problems such as high variations in intensity, indistinct edges near lung infected region and noise due to data acquisition process. Therefore, this article proposes a new COVID-19 pulmonary infection segmentation depth network referred as the Attention Gate-Dense Network- Improved Dilation Convolution-UNET (ADID-UNET). The dense network replaces convolution and maximum pooling function to enhance feature propagation and solves gradient disappearance problem. An improved dilation convolution is used to increase the receptive field of the encoder output to further obtain more edge features from the small infected regions. The integration of attention gate into the model suppresses the background and improves prediction accuracy. The experimental results show that the ADID-UNET model can accurately segment COVID-19 lung infected areas, with performance measures greater than 80% for metrics like Accuracy, Specificity and Dice Coefficient (DC). Further when compared to other state-of-the-art architectures, the proposed model showed excellent segmentation effects with a high DC and F1 score of 0.8031 and 0.82 respectively.

scholarly journals Early diagnosis of Lung Cancer with Probability of Malignancy Calculation and Automatic Segmentation of Lung CT scan Images

2020 ◽  
Vol 2 (4) ◽  
pp. 175-186
Author(s):  
Dr. Samuel Manoharan ◽  
Sathish
Keyword(s):  
Detection System ◽  
Automatic Segmentation ◽  
Pulmonary Nodules ◽  
Automated Image Analysis ◽  
Computer Aided Detection ◽  
Response Characteristics ◽  
Proposed Model ◽  
Neural Classifier ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Computer aided detection system was developed to identify the pulmonary nodules to diagnose the cancer cells. Main aim of this research enables an automated image analysis and malignancy calculation through data and CPU infrastructure. Our proposed algorithm has improvement filter to enhance the imported images and for nodule selection and neural classifier for false reduction. The proposed model is experimented in both internal and external nodules and the obtained results are shown as response characteristics curves.

Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2618-2625
Author(s):  
R. T. Subhalakshmi ◽  
S. Appavu Alias Balamurugan ◽  
S. Sasikala
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Superior Performance ◽  
Multi Scale ◽  
Proposed Model ◽  
Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

scholarly journals Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks

2019 ◽  
Vol 12 (9) ◽  
pp. 848-852 ◽  
Author(s):  
Renan Sales Barros ◽  
Manon L Tolhuisen ◽  
Anna MM Boers ◽  
Ivo Jansen ◽  
Elena Ponomareva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Convolutional Neural Networks ◽  
Infarct Volume ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Reference Standard ◽  
Computed Tomography Images

Background and purposeInfarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice.ObjectiveTo assess the value of convolutional neural networks (CNNs) in the automatic segmentation of infarct volume in follow-up CT images in a large population of patients with acute ischemic stroke.Materials and methodsWe included CT images of 1026 patients from a large pooling of patients with acute ischemic stroke. A reference standard for the infarct segmentation was generated by manual delineation. We introduce three CNN models for the segmentation of subtle, intermediate, and severe hypodense lesions. The fully automated infarct segmentation was defined as the combination of the results of these three CNNs. The results of the three-CNNs approach were compared with the results from a single CNN approach and with the reference standard segmentations.ResultsThe median infarct volume was 48 mL (IQR 15–125 mL). Comparison between the volumes of the three-CNNs approach and manually delineated infarct volumes showed excellent agreement, with an intraclass correlation coefficient (ICC) of 0.88. Even better agreement was found for severe and intermediate hypodense infarcts, with ICCs of 0.98 and 0.93, respectively. Although the number of patients used for training in the single CNN approach was much larger, the accuracy of the three-CNNs approach strongly outperformed the single CNN approach, which had an ICC of 0.34.ConclusionConvolutional neural networks are valuable and accurate in the quantitative assessment of infarct volumes, for both subtle and severe hypodense infarcts in follow-up CT images. Our proposed three-CNNs approach strongly outperforms a more straightforward single CNN approach.

scholarly journals Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

2021 ◽  
Author(s):  
Debanjan Konar ◽  
Bijaya Ketan Panigrahi ◽  
Siddhartha Bhattacharyya ◽  
Nilanjan Dey ◽  
Richard Jiang
Keyword(s):  
Automatic Segmentation ◽  
Feature Learning ◽  
Image Data ◽  
Network Models ◽  
Ct Images ◽  
Neural Network Models ◽  
Learning Network ◽  
Novel Coronavirus ◽  
Neighborhood Topology ◽  
Lung Ct

Abstract Infection of Novel Coronavirus 2019 (COVID-19) on lung cells and human respiratory systems have raised real concern to the human lives during the current pandemic spread across the world. Recent observations on CT images of human lungs infected by COVID-19 is a challenging task for the researchers in finding suitable image patterns for automatic diagnosis. In this paper, a novel semi-supervised shallow learning network model comprising Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) with Fully Connected (FC) layers is proposed for automatic segmentation followed by patch-based classifications on segmented lung CT images for the diagnosis of COVID-19 disease. The PQIS-Net model is incorporated for fully automated segmentation of lung CT scan images obviating pre-trained convolutional neural network models for feature learning. The PQIS-Net model comprises a trinity of layered structures of quantum bits inter-connected through rotation gates using an 8-connected second-order neighborhood topology for the segmentation of wide variation of local intensities of the CT images. Intensive experiments have been carried out on two publicly available lung CT image data sets thereby achieving promising segmentation outcome and diagnosis efficiency (F1-score and AUC) while compared with the state of the art pre-trained convolutional based models.

scholarly journals Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 214-221 ◽  
Author(s):  
Ke Sun ◽  
Tieyun Qian ◽  
Tong Chen ◽  
Yile Liang ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
State Of The Art ◽  
User Preferences ◽  
Short Term ◽  
Preference Modeling ◽  
Point Of Interest ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Novel Method ◽  
Real World Datasets

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

scholarly journals Genetic Algorithm based Predictive model for COVID 19 – Theoretical Model based on an evolutionary approach

2021 ◽  
Author(s):  
Srikanth Rangarajan ◽  
Srikanth Poranki ◽  
Bahgat Sammakia
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
State Of The Art ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Time Data ◽  
Proposed Model ◽  
Fitness Value ◽  
Real Time Data ◽  
Novel Method

Abstract In this manuscript we propose a novel method that models the evolution, spread and transmission of COVID 19 pandemic. The proposed model is inspired partly from the evolutionary based state of the art genetic algorithm. The rate of virus evolution, spread and transmission of the COVID 19 and its associated recovery and death rate are modeled using the principle inspired from evolutionary algorithm. Furthermore, the interaction within a community and interaction outside the community is modeled. Using this model, the maximum healthcare threshold is fixed as a constraint. Our evolutionary based model distinguishes between individuals in the population depending on the severity of their symptoms/infection based on the fitness value of the individuals. There is a need to differentiate between virus infected diagnosed (Self isolated) and virus infected non-diagnosed (Highly interacting) sub populations/group. In this study the model results does not compare the number outcomes with any actual real time data based curves. However, the results from the model demonstrates that a strict lockdown, social-distancing measures in conjunction with more number of testing and contact tracing is required to flatten the ongoing COVID-19 pandemic curve. A reproductive number of 2.4 during the initial spread of virus is predicted from the model for the randomly considered population. The proposed model has the potential to be further fine-tuned and matched accurately against real time data.

scholarly journals Dosimetric Impact of Deep Learning-Based CT Auto-Segmentation on IMRT Treatment Planning for Prostate Cancer

2021 ◽  
Author(s):  
Maria Kawula ◽  
Dinu Purice ◽  
Minglun Li ◽  
Gerome Vivar ◽  
Seyed-Ahmad Ahmadi ◽  
...  
Keyword(s):  
State Of The Art ◽  
Pearson Correlation ◽  
Automatic Segmentation ◽  
Volumetric Modulated Arc Therapy ◽  
Target Volume ◽  
Ct Images ◽  
Pass Rate ◽  
Dice Similarity Coefficient ◽  
Dose Distributions ◽  
The Impact

Abstract Background The evaluation of the automatic segmentation algorithms is commonly performed using geometric metrics, yet an evaluation based on dosimetric parameters might be more relevant in clinical practice but is still lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in intensity-modulated radiation therapy (IMRT) for prostate patients for the first time. Methods A database of 69 computed tomography (CT) images with prostate, bladder, and rectum delineations was used for single-label 3D U-Net training with dice similarity coefficient (DSC)-based loss. Volumetric modulated arc therapy (VMAT) plans have been generated for both manual and automatic segmentations with the same optimization settings. These were chosen to give consistent plans when applying perturbations to the manual segmentations. Contours were evaluated in terms of DSC, average and 95% Hausdorff distance (HD). Dose distributions were evaluated with the manual segmentation as reference using dose volume histogram (DVH) parameters and a 3%/3mm gamma-criterion with 10% dose cut-off. A Pearson correlation coefficient between DSC and dosimetric metrics, gamma index and DVH parameters, has been calculated. Results 3D U-Net based segmentation achieved a DSC of 0.87(0.03) for prostate, 0.97(0.01) for bladder and 0.89(0.04) for rectum. The mean and 95% HD were below 1.6(0.4) and below 5(4) mm, respectively. The DVH parameters V 60/65/70 Gy for the bladder and V 50/65/70 Gy for the rectum showed agreement between dose distributions within ±5% and ±2%, respectively. The DVH parameters for prostate and prostate+3mm margin (surrogate clinical target volume) showed good target coverage for the 3D U-Net segmentation with the exception of one case. The average gamma pass-rate was 85\%. A comparison between geometric and dosimetric metrics showed no strong statistically significant correlation between these metrics. Conclusions The 3D U-Net developed for this work achieved state-of-the-art geometrical performance. The study highlighted the importance of dosimetric evaluation on top of standard geometric parameters and concluded that the automatic segmentation is sufficiently accurate to assist the physicians in manually contouring organs in CT images of the male pelvic region, which is an important step towards a fully automated workflow in IMRT.

A deep fully residual convolutional neural network for segmentation in EM images

2020 ◽  
Vol 18 (03) ◽  
pp. 2050007
Author(s):  
Juanjuan He ◽  
Song Xiang ◽  
Ziqi Zhu
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Spatial Information ◽  
State Of The Art ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Post Process ◽  
Proposed Model

In standard U-net, researchers only use long skip connections to skip features from the encoding path to the decoding path in order to recover spatial information loss during downsampling. However, it would result in gradient vanishing and limit the depth of the network. To address this issue, we propose a novel deep fully residual convolutional neural network that combines the U-net with the ResNet for medical image segmentation. By applying short skip connections, this new extension of U-net decreases the amount of parameters compared to the standard U-net, although the depth of the layer is increased. We evaluate the performance of the proposed model and other state-of-the-art models on the Electron Microscopy (EM) images dataset and the Computed Tomography (CT) images dataset. The result shows that our model achieves competitive accuracy on the EM benchmark without any further post-process. Moreover, the performance of image segmentation on CT images of the lungs is improved in contrast to the standard U-net.

scholarly journals LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 158
Author(s):  
Vivek Kumar Singh ◽  
Mohamed Abdel-Nasser ◽  
Nidhi Pandey ◽  
Domenec Puig
Keyword(s):  
Deep Learning ◽  
Receptive Field ◽  
Contextual Information ◽  
Ct Images ◽  
Learning Ability ◽  
Discrete Wavelet ◽  
Field Frequency ◽  
Segmentation Methods ◽  
Lung Ct ◽  
Ct Slices

COVID-19 is a fast-growing disease all over the world, but facilities in the hospitals are restricted. Due to unavailability of an appropriate vaccine or medicine, early identification of patients suspected to have COVID-19 plays an important role in limiting the extent of disease. Lung computed tomography (CT) imaging is an alternative to the RT-PCR test for diagnosing COVID-19. Manual segmentation of lung CT images is time consuming and has several challenges, such as the high disparities in texture, size, and location of infections. Patchy ground-glass and consolidations, along with pathological changes, limit the accuracy of the existing deep learning-based CT slices segmentation methods. To cope with these issues, in this paper we propose a fully automated and efficient deep learning-based method, called LungINFseg, to segment the COVID-19 infections in lung CT images. Specifically, we propose the receptive-field-aware (RFA) module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. RFA includes convolution layers to extract COVID-19 features, dilated convolution consolidated with learnable parallel-group convolution to enlarge the receptive field, frequency domain features obtained by discrete wavelet transform, which also enlarges the receptive field, and an attention mechanism to promote COVID-19-related features. Large receptive fields could help deep learning models to learn contextual information and COVID-19 infection-related features that yield accurate segmentation results. In our experiments, we used a total of 1800+ annotated CT slices to build and test LungINFseg. We also compared LungINFseg with 13 state-of-the-art deep learning-based segmentation methods to demonstrate its effectiveness. LungINFseg achieved a dice score of 80.34% and an intersection-over-union (IoU) score of 68.77%—higher than the ones of the other 13 segmentation methods. Specifically, the dice and IoU scores of LungINFseg were 10% better than those of the popular biomedical segmentation method U-Net.

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