410 research outputs found
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images
A method for automatically quantifying emphysema regions using
High-Resolution Computed Tomography (HRCT) scans of patients with chronic
obstructive pulmonary disease (COPD) that does not require manually annotated
scans for training is presented. HRCT scans of controls and of COPD patients
with diverse disease severity are acquired at two different centers. Textural
features from co-occurrence matrices and Gaussian filter banks are used to
characterize the lung parenchyma in the scans. Two robust versions of multiple
instance learning (MIL) classifiers, miSVM and MILES, are investigated. The
classifiers are trained with the weak labels extracted from the forced
expiratory volume in one minute (FEV) and diffusing capacity of the lungs
for carbon monoxide (DLCO). At test time, the classifiers output a patient
label indicating overall COPD diagnosis and local labels indicating the
presence of emphysema. The classifier performance is compared with manual
annotations by two radiologists, a classical density based method, and
pulmonary function tests (PFTs). The miSVM classifier performed better than
MILES on both patient and emphysema classification. The classifier has a
stronger correlation with PFT than the density based method, the percentage of
emphysema in the intersection of annotations from both radiologists, and the
percentage of emphysema annotated by one of the radiologists. The correlation
between the classifier and the PFT is only outperformed by the second
radiologist. The method is therefore promising for facilitating assessment of
emphysema and reducing inter-observer variability.Comment: Accepted at PLoS ON
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
Identification of interstitial lung diseases using deep learning
The advanced medical imaging provides various advantages to both the patients and the healthcare providers. Medical Imaging truly helps the doctor to determine the inconveniences in a human body and empowers them to make better choices. Deep learning has an important role in the medical field especially for medical image analysis today. It is an advanced technique in the machine learning concept which can be used to get efficient output than using any other previous techniques. In the anticipated work deep learning is used to find the presence of interstitial lung diseases (ILD) by analyzing high-resolution computed tomography (HRCT) images and identifying the ILD category. The efficiency of the diagnosis of ILD through clinical history is less than 20%. Currently, an open chest biopsy is the best way of confirming the presence of ILD. HRCT images can be used effectively to avoid open chest biopsy and improve accuracy. In this proposed work multi-label classification is done for 17 different categories of ILD. The average accuracy of 95% is obtained by extracting features with the help of a convolutional neural network (CNN) architecture called SmallerVGGNet
Diagnosing fibrotic lung disease: When is high-resolution computed tomography sufficient to make a diagnosis of idiopathic pulmonary fibrosis?
Idiopathic pulmonary fibrosis (IPF), a progressive and fatal diffuse parenchymal lung disease, is defined pathologically by the pattern of usual interstitial pneumonia (UIP). Unfortunately, a surgical lung biopsy cannot be performed in all patients due to comorbidities that may significantly increase the morbidity and mortality of the procedure. High-resolution computed tomography (HRCT) has been put forth as a surrogate to recognize pathological UIP. The quality of the HRCT impacts the ability to make a diagnosis of UIP and varies based on the centre performing the study and patient factors. The evaluation of the HRCT includes assessing the distribution and predominance of key radiographical findings, such as honeycomb, septal thickening, traction bronchiectasis and ground glass attenuation lesions. The combination of the pattern and distribution is what leads to a diagnosis and associated confidence level. HRCT features of definite UIP (subpleural, basal predominant honeycomb with septal thickening, traction bronchiectasis and ground glass attenuation lesions) have a high specificity for the UIP pathological pattern. In such cases, surgical lung biopsy can be avoided. There are caveats to using the HRCT to diagnose IPF in isolation as a variety of chronic pulmonary interstitial diseases may progress to a UIP pattern. Referral centres with experience in diffuse parenchymal lung disease that have multidisciplinary teams encompassing clinicians, radiologists and pathologists have the highest level of agreement in diagnosing IPF.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75246/1/j.1440-1843.2009.01626.x.pd
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