Marco Favetta

Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar Region

A fully automated and three-dimensional (3D) segmentation method for the identification of the pulmonary parenchyma in thorax X-ray computed tomography (CT) datasets is proposed. It is meant to be used as pre-processing step in the computer-assisted detection (CAD) system for malignant lung nodule detection that is being developed by the Medical Applications in a Grid Infrastructure Connection (MAGIC-5) Project. In this new approach the segmentation of the external airways (trachea and bronchi), is obtained by 3D region growing with wavefront simulation and suitable stop conditions, thus allowing an accurate handling of the hilar region, notoriously difficult to be segmented. Particular attention was also devoted to checking and solving the problem of the apparent ‘fusion’ between the lungs, caused by partial-volume effects, while 3D morphology operations ensure the accurate inclusion of all the nodules (internal, pleural, and vascular) in the segmented volume. The new algorithm was initially developed and tested on a dataset of 130 CT scans from the Italung-CT trial, and was then applied to the ANODE09-competition images (55 scans) and to the LIDC database (84 scans), giving very satisfactory results. In particular, the lung contour was adequately located in 96% of the CT scans, with incorrect segmentation of the external airways in the remaining cases. Segmentation metrics were calculated that quantitatively express the consistency between automatic and manual segmentations: the mean overlap degree of the segmentation masks is 0.96 ± 0.02, and the mean and the maximum distance between the mask borders (averaged on the whole dataset) are 0.74 ± 0.05 and 4.5 ± 1.5, respectively, which confirms that the automatic segmentations quite correctly reproduce the borders traced by the radiologist. Moreover, no tissue containing internal and pleural nodules was removed in the segmentation process, so that this method proved to be fit for the use in the framework of a CAD system. Finally, in the comparison with a two-dimensional segmentation procedure, inter-slice smoothness was calculated, showing that the masks created by the 3D algorithm are significantly smoother than those calculated by the 2D-only procedure.

Generating a Minimal Set of Templates for the Hippocampal Region in MR Neuroimages

We detail a procedure for generating a set of templates for the hippocampal region in magnetic resonance (MR) images, representative of the clinical conditions of the population under investigation.

ELIMED: a new hadron therapy concept based on laser driven ion beams

Laser accelerated proton beams have been proposed to be used in different research fields. A great interest has risen for the potential replacement of conventional accelerating machines with laser-based accelerators, and in particular for the development of new concepts of more compact and cheaper hadrontherapy centers. In this context the ELIMED (ELI MEDical applications) research project has been launched by INFN-LNS and ASCR-FZU researchers within the pan-European ELI-Beamlines facility framework. The ELIMED project aims to demonstrate the potential clinical applicability of optically accelerated proton beams and to realize a laser-accelerated ion transport beamline for multi-disciplinary user applications. In this framework the eye melanoma, as for instance the uveal melanoma normally treated with 62 MeV proton beams produced by standard accelerators, will be considered as a model system to demonstrate the potential clinical use of laser-driven protons in hadrontherapy, especially because of the limited constraints in terms of proton energy and irradiation geometry for this particular tumour treatment. Several challenges, starting from laser-target interaction and beam transport development up to dosimetry and radiobiology, need to be overcome in order to reach the ELIMED final goals. A crucial role will be played by the final design and realization of a transport beamline capable to provide ion beams with proper characteristics in terms of energy spectrum and angular distribution which will allow performing dosimetric tests and biological cell irradiation. A first prototype of the transport beamline has been already designed and other transport elements are under construction in order to perform a first experimental test with the TARANIS laser system by the end of 2013. A wide international collaboration among specialists of different disciplines like Physics, Biology, Chemistry, Medicine and medical doctors coming from Europe, Japan, and the US is growing up around the ELIMED project with the aim to work on the conceptual design, technical and experimental realization of this core beamline of the ELI Beamlines facility.

An innovative lung segmentation algorithm in CT images with accurate delimitation of the hilus pulmonis

This paper proposes a new segmentation method for the delimitation of the lung parenchyma in thorax Computed-Tomography (CT) datasets, which will be used as pre-processing step in the CAD (Computer Assisted Detection) system for lung nodule detection that is being developed by the MAGIC-5 (Medical Applications in a Grid Infrastructure Connection) Collaboration. Once finished, the CAD software will run in an integrated “grid” environment, where the potentiality of distributed resources for both data and computation will be exploited. The algorithm is fully automated and three-dimensional (3D). Its most innovative part - to the best of our knowledge - is the segmentation of the external airways (trachea and bronchi), obtained by 3D region growing with wavefront simulation and suitable stop conditions. Another original element is the technique used to check and solve the problem of the apparent ‘fusion’ between the lungs, caused by partial volume effects. A general overview of the algorithm is given, with some details of the innovative parts. The results of its application to a database of about 130 high-resolution low-dose images are discussed.

BIOESOnet: A Tool for the Generation of Personalized Human Metabolic Pathways from 23andMe Exome Data.

The lowering of costs of whole exome sequencing (WES) services registered in the last two years has greatly increased the demand for managing different metabolic diseases, including autism spectrum disorders (ASD). WES allows the detection of a large part of exome single nucleotide polymorphisms (SNPs), whose expression can be in some cases modulated by epigenetics, life style and microbioma changes. However, such raw data usually needs to be manipulated in order to allow useful interpretation and analysis. We present BIOESOnet, a tool for the filtering and visualization of exome 23andMe raw data into a customized methylation pathway. The tool, available at: http://www.bionumeri.org/joomla/restricted-area/onecarbon-tool, enables a fast and extensive overview of possible mutations inside an extended metabolic pathway.

Integrated model for the analysis of two-dimensional electrophore is gel image

Proteomics is the science that studies the proteome, that is the proteic expression of the genome. Cell proteome is extremely complex, and is composed of several thousand proteins. Twodimensional polyacrylamide gel electrophoresis (2DPAGE) is widely used as a standard method to separate and display proteins in a tissue or compound with a theoretical resolution of 104 proteins simultaneously. This technique combines the resolution power of isoelectrofocalization (IEF), which distinguishes proteins by their isoelectric point (pI), with SDSPAGE (sodium dodecyl sulphate PAGE), in which proteins are separated according to their weight and molecular size. Our group is developing software algorithms for the automatic analysis of images obtained by 2DPAGE gel optical scanning; our aim is the reduction of human intervention in the analysis process, currently quite slow, operatordependent, and prone to errors. In our approach, image noise is first reduced, in order to limit false positives and protein missing. Proteins appear as dark spots on a light background, so the next step is local minima search. Then, the watershed transform is applied, which partitions the gel image into basins: each basin contains a single (recognized) minimum, but can possibly include more than one protein spot if lessdeep minima are masked by the main one. At this point, we perform a registration between the work image and an atlas image (already analyzed by a biologist), and map the atlas spot positions to the work image. Each basin is then used as a region of interest (ROI) in which the shape of the spot (or spots) is fit to a model through a χ2-minimization procedure. The coordinates of the transformed spots are used as the fit initialization parameters.