A framework for diagnosing interstitial lung diseases in HRCT: the TALISMAN project (original) (raw)
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Computer-aided diagnostic for interstitial lung diseases in HRCT: the TALISMAN project
In this paper, we describe the goals and the latest outcomes of the TALISMAN project which aims to carry out image-based diagnostic aid for interstitial lung diseases (ILDs) with secondary data integration. Prototypes of the computer tools are implemented. High correct classification rates of lung tissue regions in high-resolution computed tomography (HRCT) based on a high-quality dataset built from clinical routine suggests that the computerized analysis of HRCT image with integration of the clinical context is ready to be used for computer-aided diagnosis of ILDs. As future work, implementation of multimodal retrieval of ILD cases and the clinical evaluation of the software are planned in order to cope with clinical needs.
Image-based diagnostic aid for interstitial lung disease with secondary data integration
2007
Interstitial lung diseases (ILDs) are a relatively heterogeneous group of around 150 illnesses with often very unspecific symptoms. The most complete imaging method for the characterisation of ILDs is the high-resolution computed tomography (HRCT) of the chest but a correct interpretation of these images is difficult even for specialists as many diseases are rare and thus little experience exists. Moreover, interpreting HRCT images requires knowledge of the context defined by clinical data of the studied case. A computerised diagnostic aid tool based on HRCT images with associated medical data to retrieve similar cases of ILDs from a dedicated database can bring quick and precious information for example for emergency radiologists. The experience from a pilot project highlighted the need for detailed database containing high-quality annotations in addition to clinical data. The state of the art is studied to identify requirements for image-based diagnostic aid for interstitial lung disease with secondary data integration. The data acquisition steps are detailed. The selection of the most relevant clinical parameters is done in collaboration with lung specialists from current literature, along with knowledge bases of computer-based diagnostic decision support systems. In order to perform high-quality annotations of the interstitial lung tissue in the HRCT images an annotation software and its own file format is implemented for DICOM images. A multimedia database is implemented to store ILD cases with clinical data and annotated image series. Cases from the University & University Hospitals of Geneva (HUG) are retrospectively and prospectively collected to populate the database. Currently, 59 cases with certified diagnosis and their clinical parameters are stored in the database as well as 254 image series of which 26 have their regions of interest annotated. The available data was used to test primary visual features for the classification of lung tissue patterns. These features show good discriminative properties for the separation of five classes of visual observations.
Image based diagnostic aid system for interstitial lung diseases
Expert Systems With Applications, 2011
Automatic classification of lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) is an important stage in the construction of a computer-aided diagnosis system. In this study, we propose a new image based system for classification of lung tissue patterns. The proposed system comprises three stages. In the first stage, the parenchyma region in HRCT lung images is separated using a set of thresholding, filtering and morphological operators. In the second stage, two sets of overcomplete wavelet filters, namely discrete wavelet frames and rotated wavelet frames, are utilized to extract features from defined regions of interest (ROIs) within parenchyma. Then, in the third stage, the fuzzy k-nearest neighbor algorithm is employed to perform the pattern classification. The proposed method is tested for classifying four different lung tissue patterns (ground glass, honeycombing, reticular, and normal) selected from a database of 339 images from 17 subjects. After applying our technique to classify these patterns in isolated ROIs, we extend the classification scheme to the whole lung in order to produce quantitative scores of abnormalities in lung parenchyma of patients. The performance of the proposed method is compared with two state-of-the-art texture based methods for lung tissue characterization and is also validated against experienced observers. The average kappa statistic of the agreement between two radiologists and the computer was found to be 0.6543 where as the average kappa statistic for the inter-observer agreement was 0.6848. We also performed an experiment to show the correlation between pulmonary function test parameters and quantitative scores of computerized system. Results show that extent of HRCT findings correlates significantly with functional impairment. The computer system is shown to approach the performance of the expert observers in diagnosing regions of interest and can help to produce objective measures of abnormal patterns in lung HRCT images.
Building a reference multimedia database for interstitial lung diseases
Computerized Medical Imaging and Graphics, 2012
This paper describes the methodology used to create a multimedia collection of cases with interstitial lung diseases (ILDs) at the University Hospitals of Geneva. The dataset contains high-resolution computed tomography (HRCT) image series with three-dimensional annotated regions of pathological lung tissue along with clinical parameters from patients with pathologically proven diagnoses of ILDs. The motivations for this work is to palliate the lack of publicly available collections of ILD cases to serve as a basis for the development and evaluation of image-based computerized diagnostic aid. After 38 months of data collection, the library contains 128 patients affected with one of the 13 histological diagnoses of ILDs, 108 image series with more than 41 l of annotated lung tissue patterns as well as a comprehensive set of 99 clinical parameters related to ILDs. The database is available for research on request and after signature of a license agreement.
Diagnostics
Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring ILDs, as they can provide non-invasive, rapid, and repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of the lung parenchyma, resembling a low-magnification anatomical preparation from a histological perspective. The intrinsic contrast provided by air in HRCT enables the identification of even the subtlest morphological changes in the lung tissue. By interpreting the findings observed on HRCT, radiologists can make a differential diagnosis and provide a pattern diagnosis in collaboration with the clinical and functional data. The use of quantitative software and artificial intelligence (AI) further enhances the analysis of ILDs, providing a...
International Journal of Computer Assisted Radiology and Surgery
Purpose Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Methods Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. Results In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user’s side and on the algorithmic side. Conclusions The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the system.
Building a library of annotated pulmonary CT cases for diagnostic aid
Interstitial lung diseases (ILDs) are characterized by diverse disorders of the lung tissue with frequently confusing symptoms. The first imaging method used for diagnostics is the chest x-ray but it is not always explicit enough. High-resolution computed tomography (HRCT) of the chest contains essential visual data for the characterization of ILDs. The interpretation of HRCTs is difficult even for specialists as many diseases are rare. An imagebased diagnostic aid tool could bring precious elements to less experienced radiologists or non-chest experts. It can thus replace the search in printed reference books. The development of these tools requires creating a library of pulmonary CT cases, which incorporates annotations of regions of abnormal lung tissues. This paper details the steps for building a database of ILD cases and an annotation tool. The precision of the annotations is fundamental for the accuracy of the diagnostic aid tool. The annotation software is implemented in a web-based manner, allowing high-quality creation of regions of interest (ROIs) in any layer of a CT volume. Finally, a correct interpretation of HRCTs requires metadata of the concerned case integrated in the database. Currently, only the annotation tool and the metadata definition are available but cases are identified and will over time populate the database.
International Journal of Engineering & Technology, 2018
Automated tissues characterization helps to diagnosis the various diseases including Interstitial lung diseases (ILD). The various features and the several classifiers are used in categorize the different layers depend on the pattern presented in the image. The different types of diseases may occur in the lungs and some of the diseases happen to leave the scars. These scars can be found in the High Resolution Computed Tomography (HRCT) and have different pattern. The different diseases cause the different pattern in the images and these is classified using the efficient classifier that helps to diagnosis the diseases. In this paper, review for the many researches regarding to the classification of the different pattern from the Computed Tomography (CT) images is presented. The evaluation of the efficiency of the methods in terms of classifier and database used for the research is made. The Deep Convolution Neural Network (CNN) provides the promising classifier efficiency compared to...
Diagnostics, 2021
To evaluate the reader’s diagnostic performance against the ground truth with and without the help of a novel content-based image retrieval system (CBIR) that retrieves images with similar CT patterns from a database of 79 different interstitial lung diseases. We evaluated three novice readers’ and three resident physicians‘ (with at least three years of experience) diagnostic performance evaluating 50 different CTs featuring 10 different patterns (e.g., honeycombing, tree-in bud, ground glass, bronchiectasis, etc.) and 24 different diseases (sarcoidosis, UIP, NSIP, Aspergillosis, COVID-19 pneumonia etc.). The participants read the cases first without assistance (and without feedback regarding correctness), and with a 2-month interval in a random order with the assistance of the novel CBIR. To invoke the CBIR, a ROI is placed into the pathologic pattern by the reader and the system retrieves diseases with similar patterns. To further narrow the differential diagnosis, the readers ca...