Computer-aided detection of pulmonary nodules in low-dose CT

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical CT images with 1.25 mm slice thickness is being developed in the framework of the INFN-supported MAGIC-5 Italian project. The basic modules of our lung-CAD system, a dot enhancement filter for nodule candidate selection and a voxel-based neural classifier for false-positive finding reduction, are described. Preliminary results obtained on the so-far collected database of lung CT scans are discussed.Comment: 3 pages, 4 figures; Proceedings of the CompIMAGE - International Symposium on Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications, 20-21 Oct. 2006, Coimbra, Portuga

Automated detection of lung nodules in low-dose computed tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low-dose CT scans reconstructed with thin slice thickness (~300 slices/scan). The preliminary results are shown in terms of the FROC analysis reporting a good sensitivity (85% range) for both internal and sub-pleural nodules at an acceptable level of false positive findings (1-9 FP/scan); the sensitivity value remains very high (75% range) even at 1-6 FP/scanComment: 4 pages, 2 figures: Proceedings of the Computer Assisted Radiology and Surgery, 21th International Congress and Exhibition, Berlin, Volume 2, Supplement 1, June 2007, pp 357-35

An automated system for lung nodule detection in low-dose computed tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The results obtained on the collected database of low-dose thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.Comment: 9 pages, 9 figures; Proceedings of the SPIE Medical Imaging Conference, 17-22 February 2007, San Diego, California, USA, Vol. 6514, 65143

Lung Nodule Detection in Screening Computed Tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images with 1.25 mm slice thickness is presented. The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a neural classifier for false-positive finding reduction, are described. The results obtained on the collected database of lung CT scans are discussed.Comment: 3 pages, 4 figures; Proceedings of the IEEE NNS and MIC Conference, Oct. 29 - Nov. 4, 2006, San Diego, Californi

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.Comment: 18 pages, 12 low-resolution figure

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

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.67% with 1 false positive per scan and sensitivity of 94.19% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.Comment: Submitted to IEEE TM

Development and application in clinical practice of Computer-aided Diagnosis systems for the early detection of lung cancer

Lung cancer is the main cause of cancer-related deaths both in Europe and United States, because often it is diagnosed at late stages of the disease, when the survival rate is very low if compared to first asymptomatic stage. Lung cancer screening using annual low-dose Computed Tomography (CT) reduces lung cancer 5-year mortality by about 20% in comparison to annual screening with chest radiography. However, the detection of pulmonary nodules in low-dose chest CT scans is a very difficult task for radiologists, because of the large number (300/500) of slices to be analyzed. In order to support radiologists, researchers have developed Computer aided Detection (CAD) algorithms for the automated detection of pulmonary nodules in chest CT scans. Despite proved benefits of those systems on the radiologists detection sensitivity, the usage of CADs in clinical practice has not spread yet. The main objective of this thesis is to investigate and tackle the issues underlying this inconsistency. In particular, in Chapter 2 we introduce M5L, a fully automated Web and Cloud-based CAD for the automated detection of pulmonary nodules in chest CT scans. This system introduces a new paradigm in clinical practice, by making available CAD systems without requiring to radiologists any additional software and hardware installation. The proposed solution provides an innovative cost-effective approach for clinical structures. In Chapter 3 we present our international challenge aiming at a large-scale validation of state-of-the-art CAD systems. We also investigate and prove how the combination of different CAD systems reaches performances much higher than any best stand-alone system developed so far. Our results open the possibility to introduce in clinical practice very high-performing CAD systems, which miss a tiny fraction of clinically relevant nodules. Finally, we tested the performance of M5L on clinical data-sets. In chapter 4 we present the results of its clinical validation, which prove the positive impact of CAD as second reader in the diagnosis of pulmonary metastases on oncological patients with extra-thoracic cancers. The proposed approaches have the potential to exploit at best the features of different algorithms, developed independently, for any possible clinical application, setting a collaborative environment for algorithm comparison, combination, clinical validation and, if all of the above were successful, clinical practice