scholarly journals Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework

2021 ◽  
Author(s):  
Li Wang ◽  
Wenlong Ding ◽  
Yan Mo ◽  
Dejun Shi ◽  
Shuo Zhang ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Deep Learning ◽  
Lung Disease ◽  
Nontuberculous Mycobacteria ◽  
Ct Images ◽  
Learning Framework

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

scholarly journals ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 268
Author(s):  
Yeganeh Jalali ◽  
Mansoor Fateh ◽  
Mohsen Rezvani ◽  
Vahid Abolghasemi ◽  
Mohammad Hossein Anisi
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Great Success ◽  
Morphological Operations ◽  
Ct Image ◽  
Feature Maps ◽  
Learning Framework ◽  
Lung Ct

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved.

Deep learning for screening of interstitial lung disease patterns in high-resolution CT images

2020 ◽  
Vol 75 (6) ◽  
pp. 481.e1-481.e8
Author(s):  
S. Agarwala ◽  
M. Kale ◽  
D. Kumar ◽  
R. Swaroop ◽  
A. Kumar ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Ct Images ◽  
High Resolution Ct ◽  
Disease Patterns

Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images

2020 ◽  
Vol 191 ◽  
pp. 105387
Author(s):  
Floris Heutink ◽  
Valentin Koch ◽  
Berit Verbist ◽  
Willem Jan van der Woude ◽  
Emmanuel Mylanus ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Ct Images ◽  
Learning Framework ◽  
Multi Scale ◽  
High Resolution Ct

scholarly journals Recent advances in nontuberculous mycobacterial lung infections

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1710 ◽  
Author(s):  
David Horne ◽  
Shawn Skerrett
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Natural History ◽  
Lung Disease ◽  
Chronic Lung Disease ◽  
Nontuberculous Mycobacteria ◽  
Mycobacterium Leprae ◽  
Mycobacterium Abscessus ◽  
Recent Developments ◽  
Lung Infections ◽  
Ntm Lung Disease

Nontuberculous mycobacteria (NTM) are members of the Mycobacterium genus other than Mycobacterium tuberculosis complex and Mycobacterium leprae. NTM are widely distributed in the environment and are increasingly recognized as causes of chronic lung disease that can be challenging to treat. In this brief review, we consider recent developments in the ecology, epidemiology, natural history, and treatment of NTM lung disease with a focus on Mycobacterium avium complex (MAC) and Mycobacterium abscessus complex.

A novel deep learning framework for lung nodule detection in 3d CT images

2021 ◽  
Author(s):  
Reza Majidpourkhoei ◽  
Mehdi Alilou ◽  
Kambiz Majidzadeh ◽  
Amin Babazadehsangar
Keyword(s):  
Deep Learning ◽  
Lung Nodule ◽  
Ct Images ◽  
3D Ct ◽  
Learning Framework ◽  
Lung Nodule Detection ◽  
Nodule Detection

Clinically applicable deep learning framework for organs at risk delineation in CT images

2019 ◽  
Vol 1 (10) ◽  
pp. 480-491 ◽  
Author(s):  
Hao Tang ◽  
Xuming Chen ◽  
Yang Liu ◽  
Zhipeng Lu ◽  
Junhua You ◽  
...  
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Organs At Risk ◽  
Ct Images ◽  
Learning Framework

scholarly journals Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction

Tomography ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 932-949
Author(s):  
Chang Sun ◽  
Yitong Liu ◽  
Hongwen Yang
Keyword(s):  
Deep Learning ◽  
Structural Similarity ◽  
Frequency Component ◽  
Ct Images ◽  
Training Dataset ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Image Domain ◽  
Learning Framework ◽  
Reconstruction Methods

Sparse-view CT reconstruction is a fundamental task in computed tomography to overcome undesired artifacts and recover the details of textual structure in degraded CT images. Recently, many deep learning-based networks have achieved desirable performances compared to iterative reconstruction algorithms. However, the performance of these methods may severely deteriorate when the degradation strength of the test image is not consistent with that of the training dataset. In addition, these methods do not pay enough attention to the characteristics of different degradation levels, so solely extending the training dataset with multiple degraded images is also not effective. Although training plentiful models in terms of each degradation level can mitigate this problem, extensive parameter storage is involved. Accordingly, in this paper, we focused on sparse-view CT reconstruction for multiple degradation levels. We propose a single degradation-aware deep learning framework to predict clear CT images by understanding the disparity of degradation in both the frequency domain and image domain. The dual-domain procedure can perform particular operations at different degradation levels in frequency component recovery and spatial details reconstruction. The peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and visual results demonstrate that our method outperformed the classical deep learning-based reconstruction methods in terms of effectiveness and scalability.

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