G. Gargano

Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking. The performance of six algorithms for which results are available are compared; five from academic groups and one commercially available system. A method to combine the output of multiple systems is proposed. Results show a substantial performance difference between algorithms, and demonstrate that combining the output of algorithms leads to marked performance improvements.

The MAGIC-5 Project: medical applications on a GRID infrastructure connection

The MAGIC-5 Project aims at developing computer aided detection (CAD) software for medical applications on distributed databases by means of a GRID infrastructure connection. The use of automatic systems for analyzing medical images is of paramount importance in the screening programs, due to the huge amount of data to check. Examples are: mammographies for breast cancer detection, computed-tomography (CT) images for lung cancer analysis, and the positron emission tomography (PET) imaging for the early diagnosis of the Alzheimer disease. The need for acquiring and analyzing data stored in different locations requires a GRID approach of distributed computing system and associated data management. The GRID technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays actually associated to the screening programs. From this point of view, the MAGIC-5 Collaboration can be seen as a group of distributed users sharing their resources for implementing different virtual organizations (VO), each one aiming at developing screening programs, tele-training, tele-diagnosis and epidemiologic studies for a particular pathology.

Computer aided diagnosis for lung CT using artificial life models

With the present paper we introduce a new computer assisted detection method for lung cancer from CT images. The algorithm is based on different algorithms like: 3D region growing, active contour and shape models, centre of maximal balls but we can say that at the core of our approach are the biological models of ants also known as artificial life models. In the first step of the algorithm the images are undergoing a 3D region growing for identifying the ribcage. Once the ribcage is identified an active contour is used in order to build a confined area for the incoming ants that are deployed to make clean and accurate reconstruction of the bronchial and vascular tree. Next the branches of the newly reconstructed trees are checked to see whether they include nodules or not by using active shape models and to also to see if there are any nodules attached to the pleura of the lungs (centre of maximal balls). The next step is to remove the trees in order to provide a cleaner algorithm for localizing the nodules which is achieved by applying snakes and dot enhancement algorithms.

A novel Active Contour Model algorithm for contour detection in complex objects

A new active contour model (ACM) algorithm for the detection of the contour of bi-dimensional regions is presented. The algorithm is based on the simulation of an elastic band glued to the contour of the region under analysis. As a result a local convex hull is obtained, where the radius of the concave regions included by the elastic band is defined by properly tuning a parameter. A dedicated application to medical images is presented. The algorithm is part of a segmentation system able to extract the lung volume from 3D CT scans. The effectiveness of the algorithm is evaluated on a database of 15 low-dose CT scans (about 320 sectional images per CT), including 26 nodules. No pathological structure is missing after the lung volume segmentation, while a reduction of the volume to analyze is obtained to about 15% of the total volume of the original CT scan, and 25% of the chest volume.

Methodology for automated detection of internal and juxtapleural lung nodules in computed tomography images.

Automatic and consistent registration framework for temporal pairs of mammograms in application to breast cancer risk assessment due to Hormone Replacement Therapy (HRT) G. Karemore, I. Arganda-Carreras, M. Nielsen University of Copenhagen, Denmark Nordic Bioscience A/S, Herlev, Denmark Universidad Autónoma de Madrid, Spain

Artificial life models in lung CTs

With the present paper we introduce a new Computer Assisted Detection method for Lung Cancer in CT images. The algorithm is based on several sub-modules: 3D Region Growing, Active Contour And Shape Models, Centre of Maximal Balls, but the core of our approach are Biological Models of ants known as Artificial Life models. In the first step of the algorithm images undergo a 3D region growing procedure for identifying the ribs cage; then Active Contour Models are used in order to build a confined area for the incoming ants that are deployed to make clean and accurate reconstruction of the bronchial and vascular tree, which is removed from the image just before checking for nodules.

A new CAD system for lung nodule detection on low dose CT validated on publicly research database

Purpose Conventional chest X-ray is still the most common radiological imaging to detect pulmonary metastases and nodules. The detection rate of such nodules, however, is limited due to different experiences of the radiologists and depends furthermore critically on the image quality. The role of conventional X-ray as a screening tool is critically discussed [1]. The enhanced and automated detection of pulmonary nodules but also the automated detection of other alterations of the lung including fibrotic and emphysematic structures is, thus of essential importance. In order to achieve a reliable and accurate analysis computer-based image analysis quality might offer additional help. Currently available CAD-solutions will shortly be discussed within this invited lecture with special focus on clinical results and further needs of development. Methods Recently established CAD-solutions offer the opportunity of an automated detection of pulmonary nodules, there is no software-based limitation regarding size or location of the nodules and masses [2,3,4], see Figs. 1, 2, 3. The most common software solutions do not allow an automated size calculation, yet. Application of CAD is not limited to p-aviews but also applicable to portable images. Detection on lateral views are not available in all CAD-systems on the market, yet. Morphological parameters are not automatically quantified, yet. Thus CAD is mainly focussed on the detection of any nodules, for the time being. In order to improve image quality, as an additional tool, a bone and rib-reducing software has been established [5], see Fig. 1. Effect of CAD was analyzed focussing on potential influencing parameters, including histopathology, size, location, imaging quality. Additionally effects of CAD on detection rate and observer reliability were analyzed and typical imaging characteristics were classified as most common reasons for false positive or negative analyses. Finally, the role of currently available CAD in the detection of conventionally occult malignant lesions was evaluated. Personal analyses base on 550 chest X-rays (300 proven malignancies, 100 benign cases including portables, 100 CT-proven unsuspicious cases and 50 cases with missed/occult malignant lesions). Analysis was performed using OnGuard Europe 5.0, Riverain Medical USA including Softview mode. CAD marks (region of interest, ROI, were matched to CT and histopathology in consensus by two experienced radiologists. Sizes of the lesions were scored as S1: up to 10mm, S2: up to 20mm; S3: up to 30mm; S4: up to 40mm; S5: up to 50mm; S6 all other lesions. Results Own results

Ant Colonies for the reconstruction of artificial 3D Objects

Ant Colony Models are artificial simulations of real ant colonies [1], [2]. The way real ants behave in nature inspire cooperation and competition strategies for virtual agent: the emergence of intelligent behavior and swarm-based self- organization can be used to solve difficult problems. In this work an Ant Colony Model for 3D objects reconstruction is presented. The accuracy in reconstructing 3D object is tested on artificial 3D objects and on 10 real Computed Tomography (CT) images of human lungs.