4,400 research outputs found
Classification of interstitial lung disease patterns with topological texture features
Topological texture features were compared in their ability to classify
morphological patterns known as 'honeycombing' that are considered indicative
for the presence of fibrotic interstitial lung diseases in high-resolution
computed tomography (HRCT) images. For 14 patients with known occurrence of
honey-combing, a stack of 70 axial, lung kernel reconstructed images were
acquired from HRCT chest exams. A set of 241 regions of interest of both
healthy and pathological (89) lung tissue were identified by an experienced
radiologist. Texture features were extracted using six properties calculated
from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and
three Minkowski Functionals (MFs, e.g. MF.euler). A k-nearest-neighbor (k-NN)
classifier and a Multilayer Radial Basis Functions Network (RBFN) were
optimized in a 10-fold cross-validation for each texture vector, and the
classification accuracy was calculated on independent test sets as a
quantitative measure of automated tissue characterization. A Wilcoxon
signed-rank test was used to compare two accuracy distributions and the
significance thresholds were adjusted for multiple comparisons by the
Bonferroni correction. The best classification results were obtained by the MF
features, which performed significantly better than all the standard GLCM and
MD features (p < 0.005) for both classifiers. The highest accuracy was found
for MF.euler (97.5%, 96.6%; for the k-NN and RBFN classifier, respectively).
The best standard texture features were the GLCM features 'homogeneity' (91.8%,
87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced
topological texture features can provide superior classification performance in
computer-assisted diagnosis of interstitial lung diseases when compared to
standard texture analysis methods.Comment: 8 pages, 5 figures, Proceedings SPIE Medical Imaging 201
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Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach
Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule
classification focusing on (i) usefulness of gradient tree boosting (XGBoost)
and (ii) effectiveness of parameter optimization using Bayesian optimization
(Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung
cancers and 37 benign lung nodules) were included from public databases of CT
images. A variant of local binary pattern was used for calculating feature
vectors. Support vector machine (SVM) or XGBoost was trained using the feature
vectors and their labels. TPE or random search was used for parameter
optimization of SVM and XGBoost. Leave-one-out cross-validation was used for
optimizing and evaluating the performance of our CADx system. Performance was
evaluated using area under the curve (AUC) of receiver operating characteristic
analysis. AUC was calculated 10 times, and its average was obtained. The best
averaged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both were
obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters
for achieving high AUC were obtained with fewer numbers of trials when using
TPE, compared with random search. In conclusion, XGBoost was better than SVM
for classifying lung nodules. TPE was more efficient than random search for
parameter optimization.Comment: 29 pages, 4 figure
Classification of lung disease in HRCT scans using integral geometry measures and functional data analysis
A framework for classification of chronic lung disease from high-resolution CT scans is presented. We use a set of features which measure the local morphology and topology of the 3D voxels within the lung parenchyma and apply functional data classification to the extracted features. We introduce the measures, Minkowski functionals, which derive from integral geometry and show results of classification on lungs containing various stages of chronic lung disease: emphysema, fibrosis and honey-combing. Once trained, the presented method is shown to be efficient and specific at characterising the distribution of disease in HRCT slices
Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks
Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial
for making treatment decisions, but can be challenging even for experienced
radiologists. The diagnostic procedure is based on the detection and
recognition of the different ILD pathologies in thoracic CT scans, yet their
manifestation often appears similar. In this study, we propose the use of a
deep purely convolutional neural network for the semantic segmentation of ILD
patterns, as the basic component of a computer aided diagnosis (CAD) system for
ILDs. The proposed CNN, which consists of convolutional layers with dilated
filters, takes as input a lung CT image of arbitrary size and outputs the
corresponding label map. We trained and tested the network on a dataset of 172
sparsely annotated CT scans, within a cross-validation scheme. The training was
performed in an end-to-end and semi-supervised fashion, utilizing both labeled
and non-labeled image regions. The experimental results show significant
performance improvement with respect to the state of the art
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