0000000000117750
AUTHOR
Alessandra Retico
MULTI-SCALE ANALYSIS OF LUNG COMPUTED TOMOGRAPHY IMAGES
A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.
Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples
Abstract Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-valida…
A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia
Abstract Background The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. Methods LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived…
A completely automated CAD system for mass detection in a large mammographic database
Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing secon…
Preprocessing methods for nodule detection in lung CT
Abstract The use of automatic systems in the analysis of medical images has proven to be very useful to radiologists, especially in the framework of screening programs, in which radiologists make their first diagnosis on the basis of images only, most of those corresponding to healthy patients, and have to distinguish pathological findings from non-pathological ones at an early stage. In particular, we are developing preprocessing methods to be applied for pulmonary nodule Computer Aided Detection in low-dose lung Multi Slice CT (computed tomography) images.
Detection and classification of microcalcifications clusters in digitized mammograms
In the present paper we discuss a new approach for the detection of microcalcification clusters, based on neural networks and developed as part of the MAGIC-5 project, an INFN-funded program which aims at the development and implementation of CAD algorithms in a GRID-based distributed environment. The proposed approach has as its roots the desire to maximize the rejection of background during the analytical pre-processing stage, in order to train and test the neural network with as clean as possible a sample and therefore maximize its performance. The algorithm is composed of three modules: the image pre-processing, the feature extraction component and the Backpropagation Neural Network mod…
Mammogram Segmentation by Contour Searching and Mass Lesions Classification with Neural Network
The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, an algorithm for detecting masses in mammographic images will be presented. The database consists of 3762 digital images acquired in several hospitals belonging to the MAGIC-5 collaboration (Medical Applications on a Grid Infrastructure Connection). A reduction of the whole image's area under investigation is achieved through a segmentation process, by means of a ROI Hunter algorithm, without loss of meaningful information. In the following classification step, feature extraction plays a fundamental role: some features give geometrical information, other ones provide shape parameters.…
GPCALMA: A Grid-based tool for mammographic screening
The next generation of High Energy Physics (HEP) experiments requires a GRID approach to a distributed computing system and the associated data management: the key concept is the Virtual Organisation (VO), a group of distributed users with a common goal and the will to share their resources. A similar approach is being applied to a group of Hospitals which joined the GPCALMA project (Grid Platform for Computer Assisted Library for MAmmography), which will allow common screening programs for early diagnosis of breast and, in the future, lung cancer. HEP techniques come into play in writing the application code, which makes use of neural networks for the image analysis and proved to be useful…
GPCALMA, a mammographic CAD in a GRID connection
Purpose of this work is the development of an automatic system which could be useful for radiologists in the investigation of breast cancer. A breast neoplasia is often marked by the presence of microcalcifications and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. GPCALMA (Grid Platform Computer Assisted Library for MAmmography), a collaboration among italian physicists and radiologists, has built a large distributed database of digitized mammographic images (at this moment about 5500 images corresponding to 1650 patients). This collaboration has developed a CAD (Computer Aided Detection) system which, installed in an integrated…
A Theoretical Prediction of the Bs-Meson Lifetime Difference
We present the results of a quenched lattice calculation of the operator matrix elements relevant for predicting the Bs width difference. Our main result is (\Delta\Gamma_Bs/\Gamma_Bs)= (4.7 +/- 1.5 +/- 1.6) 10^(-2), obtained from the ratio of matrix elements, R(m_b)=/=-0.93(3)^(+0.00)_(-0.01). R(m_b) was evaluated from the two relevant B-parameters, B_S^{MSbar}(m_b)=0.86(2)^(+0.02)_(-0.03) and B_Bs^{MSbar}(m_b) = 0.91(3)^(+0.00)_(-0.06), which we computed in our simulation.
MAGIC-5: an Italian mammographic database of digitised images for research
The implementation of a database of digitised mammograms is discussed. The digitised images were collected beginning in 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals as a first step in developing and implementing a computer-aided detection (CAD) system. All 3,369 mammograms were collected from 967 patients and classified according to lesion type and morphology, breast tissue and pathology type. A dedicated graphical user interface was developed to visualise and process mammograms to support the medical diagnosis directly on a high-resolution screen. The database has been the starting point for developing other medical imaging applications,…
A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model
A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce…