- Airway Segmentation and Analysis from Computed Tomography

2016 ◽  
pp. 164-201 ◽  
Keyword(s):  
Computed Tomography ◽  
Airway Segmentation

Improving airway segmentation in computed tomography using leak detection with convolutional networks

2017 ◽  
Vol 36 ◽  
pp. 52-60 ◽  
Author(s):  
Jean-Paul Charbonnier ◽  
Eva M. van Rikxoort ◽  
Arnaud A.A. Setio ◽  
Cornelia M. Schaefer-Prokop ◽  
Bram van Ginneken ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Leak Detection ◽  
Convolutional Networks ◽  
Airway Segmentation

Positive Airway Pressure to Enhance Computed Tomography Imaging for Airway Segmentation for Virtual Bronchoscopic Navigation

Respiration ◽  
2018 ◽  
Vol 96 (6) ◽  
pp. 525-534 ◽  
Author(s):  
Marta Diez-Ferrer ◽  
Debora Gil ◽  
Cristian Tebe ◽  
Carles Sanchez ◽  
Noelia Cubero ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Airway Pressure ◽  
Positive Airway Pressure ◽  
Computed Tomography Imaging ◽  
Tomography Imaging ◽  
Airway Segmentation

scholarly journals Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Antonio Garcia-Uceda ◽  
Raghavendra Selvan ◽  
Zaigham Saghir ◽  
Harm A. W. M. Tiddens ◽  
Marleen de Bruijne
Keyword(s):  
Computed Tomography ◽  
Lung Cancer Screening ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Sensitivity Score ◽  
Thoracic Computed Tomography ◽  
Cancer Screening Trial ◽  
Airway Segmentation ◽  
Pass Through ◽  
Different Characteristics

AbstractThis paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: (1) a dataset of pediatric patients including subjects with cystic fibrosis, (2) a subset of the Danish Lung Cancer Screening Trial, including subjects with chronic obstructive pulmonary disease, and (3) the EXACT’09 public dataset. We compared our method with other state-of-the-art airway segmentation methods, including relevant learning-based methods in the literature evaluated on the EXACT’09 data. We show that our method can extract highly complete airway trees with few false positive errors, on scans from both healthy and diseased subjects, and also that the method generalizes well across different datasets. On the EXACT’09 test set, our method achieved the second highest sensitivity score among all methods that reported good specificity.

Impact of image pre-processing methods on computed tomography radiomics features in chronic obstructive pulmonary disease

2021 ◽  
Author(s):  
Ryan C Au ◽  
Wan C Tan ◽  
Jean Bourbeau ◽  
James C Hogg ◽  
Miranda Kirby
Keyword(s):  
Computed Tomography ◽  
Chronic Obstructive Pulmonary Disease ◽  
Logistic Regression ◽  
Lung Function ◽  
Chronic Obstructive ◽  
Processing Methods ◽  
Obstructive Pulmonary Disease ◽  
Processing Pipeline ◽  
Airway Segmentation ◽  
Processing Techniques

Abstract Computed tomography (CT) imaging texture-based radiomics analysis can be used to assess chronic obstructive pulmonary disease (COPD). However, different image pre-processing methods are commonly used, and how these different methods impact radiomics features and lung disease assessment, is unknown. The purpose of this study was to develop an image pre-processing pipeline to investigate how various pre-processing combinations impact radiomics features and their use for COPD assessment. Spirometry and CT images were obtained from the multi-centered Canadian Cohort of Obstructive Lung Disease study. Participants were divided based on assessment site and were further dichotomized as No COPD or COPD within their participant groups. An image pre-processing pipeline was developed, calculating 32 grey level co-occurrence matrix radiomics features. The pipeline included lung segmentation, airway segmentation or no segmentation, image resampling or no resampling, and either no pre-processing, binning, edgmentation, or thresholding pre-processing techniques. A three-way analysis of variance was used for method comparison. A nested 10-fold cross validation using logistic regression and multiple linear regression models were constructed to classify COPD and assess correlation with lung function, respectively. Logistic regression performance was evaluated using the area under the receiver operating characteristic curve (AUC). A total of 1210 participants (Sites 1-8: No COPD: n=447, COPD: n=413; and Site 9: No COPD: n=155, COPD: n=195) were evaluated. Between the two participant groups, at least 16/32 features were different between airway segmentation/no segmentation (P≤0.04), at least 29/32 features were different between no resampling/resampling (P≤0.04), and 32/32 features were different between the pre-processing techniques (P<0.0001). Features generated using the resampling/edgmentation and resampling/thresholding pre-processing combinations, regardless of airway segmentation, performed the best in COPD classification (AUC≥0.718), and explained the most variance with lung function (R2≥0.353). Therefore, the image pre-processing methods completed prior to CT radiomics feature extraction significantly impacted extracted features and their ability to assess COPD.

X-ray microtomography

1988 ◽  
Vol 46 ◽  
pp. 998-999
Author(s):  
H.W. Deckman ◽  
B.F. Flannery ◽  
J.H. Dunsmuir ◽  
K.D' Amico
Keyword(s):  
Computed Tomography ◽  
Internal Structure ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Tissue Structure ◽  
Individual Element ◽  
X Ray ◽  
Non Invasive ◽  
Panoramic Imaging ◽  
Three Dimensional Images

We have developed a new X-ray microscope which produces complete three dimensional images of samples. The microscope operates by performing X-ray tomography with unprecedented resolution. Tomography is a non-invasive imaging technique that creates maps of the internal structure of samples from measurement of the attenuation of penetrating radiation. As conventionally practiced in medical Computed Tomography (CT), radiologists produce maps of bone and tissue structure in several planar sections that reveal features with 1mm resolution and 1% contrast. Microtomography extends the capability of CT in several ways. First, the resolution which approaches one micron, is one thousand times higher than that of the medical CT. Second, our approach acquires and analyses the data in a panoramic imaging format that directly produces three-dimensional maps in a series of contiguous stacked planes. Typical maps available today consist of three hundred planar sections each containing 512x512 pixels. Finally, and perhaps of most import scientifically, microtomography using a synchrotron X-ray source, allows us to generate maps of individual element.

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