Anatomy-Guided Lung Lobe Segmentation in X-Ray CT Images

In anatomy, the lung can be divided by lung fissures into several pulmonary lobe units with specific functions. Identifying the lung lobes and the distribution of various diseases among different lung lobes from CT images is important for disease diagnosis and tracking after recovery. In order to solve the problems of low tubular structure segmentation accuracy and long algorithm time in segmenting lung lobes based on lung anatomical structure information, we propose a segmentation algorithm based on lung fissure surface classification using a point cloud region growing approach. We cluster the pulmonary fissures, transformed into point cloud data, according to the differences in the pulmonary fissure surface normal vector and curvature estimated by principal component analysis. Then, a multistage spline surface fitting method is used to fill and expand the lung fissure surface to realize the lung lobe segmentation. The proposed approach was qualitatively and quantitatively evaluated on a public dataset from Lobe and Lung Analysis 2011 (LOLA11), and obtained an overall score of 0.84. Although our approach achieved a slightly lower overall score compared to the deep learning based methods (LobeNet_V2 and V-net), the inter-lobe boundaries from our approach were more accurate for the CT images with visible lung fissures.

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Pulmonary Lobe Segmentation in CT Images Based on Lung Anatomy Knowledge

Mathematical Problems in Engineering ◽

10.1155/2021/5588629 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Yuanyuan Peng ◽

Hualan Zhong ◽

Zheng Xu ◽

Hongbin Tu ◽

Xiong Li ◽

...

Keyword(s):

Left Lung ◽

Region Of Interest ◽

Ground Truth ◽

Ct Images ◽

Lung Lobe ◽

Lung Anatomy ◽

Lobe Segmentation ◽

The Right ◽

Anatomy Knowledge ◽

High Median

In computed tomography (CT) images, pulmonary lobe segmentation is an arduous task due to its complex structures. To remedy the problem, we introduce a new framework based on lung anatomy knowledge for lung lobe segmentation. Firstly, the priori knowledge of lung anatomy is used to identify the fissure region of interest. Then, an oriented derivative of stick filter is applied to isolate plate-like structures from clutters for lobar fissure verification. Finally, a surface fitting model is employed to complete the incomplete fissure surface for lung lobe segmentation. Compared with manually segmented fissure references, the designed approach obtained a high median F1-score of 0.8865 in the left lung and obtained a high median F1-score of 0.9200 in the right lung. The average percentages of the segmented lung lobes in the lung lobe ground truth are 0.960, 0.989, 0.973, 0.920, and 0.985 for the left upper, left lower, right upper, right middle, and right lower lobes, respectively. The perfect performance of the proposed scheme is tested by visual inspection and quantitative evaluation.

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Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images

Pattern Recognition ◽

10.1016/j.patcog.2021.107828 ◽

2021 ◽

Vol 113 ◽

pp. 107828 ◽

Cited By ~ 1

Author(s):

Kelei He ◽

Wei Zhao ◽

Xingzhi Xie ◽

Wen Ji ◽

Mingxia Liu ◽

...

Keyword(s):

Ct Images ◽

Severity Assessment ◽

Lung Lobe ◽

Lobe Segmentation

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Quality Improvement of X-ray CT Images by Estimation of the Mechanical Misalignment in a Projection Image Using an Iterative Method

IEEJ Transactions on Industry Applications ◽

10.1541/ieejias.137.213 ◽

2017 ◽

Vol 137 (3) ◽

pp. 213-219

Author(s):

Kenji Noguchi ◽

Satoshi Takahashi ◽

Daisuke Suzuki ◽

Atsushi Teramoto

Keyword(s):

Quality Improvement ◽

Iterative Method ◽

Ct Images ◽

X Ray ◽

Projection Image

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Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽

10.3390/en14154595 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4595

Author(s):

Parisa Asadi ◽

Lauren E. Beckingham

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Ct Images ◽

Ct Imaging ◽

Learning Method ◽

Learning Methods ◽

X Ray ◽

Machine Learning Methods ◽

Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images

Applied Sciences ◽

10.3390/app11094233 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4233

Author(s):

Biprodip Pal ◽

Debashis Gupta ◽

Md. Rashed-Al-Mahfuz ◽

Salem A. Alyami ◽

Mohammad Ali Moni

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Chest Radiography ◽

High Sensitivity ◽

Ct Images ◽

Classification Performance ◽

Learning Models ◽

X Ray ◽

Image Capturing ◽

Fast Gradient

The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40% in the case of CT images for VGG16 and Inception-v3, respectively, without any human-recognizable perturbation effects in the adversarial images. Finally, we analyzed that correct class probability of any test image which is supposed to be 1, can drop for both considered models and with increased perturbation; it can drop to 0.24 and 0.17 for the VGG16 model in cases of X-ray and CT images, respectively. Thus, despite the need for data sharing and automated diagnosis, practical deployment of such program requires more robustness.

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