6,810 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
PadChest: A large chest x-ray image dataset with multi-label annotated reports
We present a labeled large-scale, high resolution chest x-ray dataset for the
automated exploration of medical images along with their associated reports.
This dataset includes more than 160,000 images obtained from 67,000 patients
that were interpreted and reported by radiologists at Hospital San Juan
Hospital (Spain) from 2009 to 2017, covering six different position views and
additional information on image acquisition and patient demography. The reports
were labeled with 174 different radiographic findings, 19 differential
diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and
mapped onto standard Unified Medical Language System (UMLS) terminology. Of
these reports, 27% were manually annotated by trained physicians and the
remaining set was labeled using a supervised method based on a recurrent neural
network with attention mechanisms. The labels generated were then validated in
an independent test set achieving a 0.93 Micro-F1 score. To the best of our
knowledge, this is one of the largest public chest x-ray database suitable for
training supervised models concerning radiographs, and the first to contain
radiographic reports in Spanish. The PadChest dataset can be downloaded from
http://bimcv.cipf.es/bimcv-projects/padchest/
Quantitative CT analysis in ILD and use of artificial intelligence on imaging of ILD
Advances in computer technology over the past decade, particularly in the field of medical image analysis, have permitted the identification, characterisation and quantitation of abnormalities that can be used to diagnose disease or determine disease severity. On CT imaging performed in patients with ILD, deep-learning computer algorithms now demonstrate comparable performance with trained observers in the identification of a UIP pattern, which is associated with a poor prognosis in several fibrosing ILDs. Computer tools that quantify individual voxel-level CT features have also come of age and can predict mortality with greater power than visual CT analysis scores. As these tools become more established, they have the potential to improve the sensitivity with which minor degrees of disease progression are identified. Currently, PFTs are the gold standard measure used to assess clinical deterioration. However, the variation associated with pulmonary function measurements may mask the presence of small but genuine functional decline, which in the future could be confirmed by computer tools. The current chapter will describe the latest advances in quantitative CT analysis and deep learning as related to ILDs and suggest potential future directions for this rapidly advancing field
Lung Pattern Analysis using Artificial Intelligence for the Diagnosis Support of Interstitial Lung Diseases
Interstitial lung diseases (ILDs) is a group of more than 200 chronic lung disorders characterized by inflammation and scarring of the lung tissue that leads to respiratory failure. Although ILD is a heterogeneous group of histologically distinct diseases, most of them exhibit similar clinical presentations and their diagnosis often presents a diagnostic dilemma. Early diagnosis is crucial for making treatment decisions, while misdiagnosis may lead to life-threatening complications. If a final diagnosis cannot be reached with the high resolution computed tomography scan, additional invasive procedures are required (e.g. bronchoalveolar lavage, surgical biopsy). The aim of this PhD thesis was to investigate the components of a computational system that will assist radiologists with the diagnosis of ILDs, while avoiding the dangerous, expensive and time-consuming invasive biopsies. The appropriate interpretation of the available radiological data combined with clinical/biochemical information can provide a reliable diagnosis, able to improve the diagnostic accuracy of the radiologists.
In this thesis, we introduce two convolutional neural networks particularly designed for ILDs and a training scheme that employs knowledge transfer from the similar domain of general texture classification for performance enhancement. Moreover, we investigate the clinical relevance of breathing information for disease classification. The breathing information is quantified as a deformation field between inhale-exhale lung images using a novel 3D convolutional neural network for medical image registration. Finally, we design and evaluate the final end-to-end computational system for ILD classification using lung anatomy segmentation algorithms from the literature and the proposed ILD quantification neural networks. Deep learning approaches have been mostly investigated for all the aforementioned steps, while the results demonstrated their potential in analyzing lung images
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
An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans
COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic
outbreak all over the world with exponential increasing of confirmed cases and,
unfortunately, deaths. In this work we propose an AI-powered pipeline, based on
the deep-learning paradigm, for automated COVID-19 detection and lesion
categorization from CT scans. We first propose a new segmentation module aimed
at identifying automatically lung parenchyma and lobes. Next, we combined such
segmentation network with classification networks for COVID-19 identification
and lesion categorization. We compare the obtained classification results with
those obtained by three expert radiologists on a dataset consisting of 162 CT
scans. Results showed a sensitivity of 90\% and a specificity of 93.5% for
COVID-19 detection, outperforming those yielded by the expert radiologists, and
an average lesion categorization accuracy of over 84%. Results also show that a
significant role is played by prior lung and lobe segmentation that allowed us
to enhance performance by over 20 percent points. The interpretation of the
trained AI models, moreover, reveals that the most significant areas for
supporting the decision on COVID-19 identification are consistent with the
lesions clinically associated to the virus, i.e., crazy paving, consolidation
and ground glass. This means that the artificial models are able to
discriminate a positive patient from a negative one (both controls and patients
with interstitial pneumonia tested negative to COVID) by evaluating the
presence of those lesions into CT scans. Finally, the AI models are integrated
into a user-friendly GUI to support AI explainability for radiologists, which
is publicly available at http://perceivelab.com/covid-ai
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