3-D object segmentation is an important and challenging topic in computer vision that could be tackled with artificial life models. A Channeler Ant Model (CAM), based on the natural ant capabilities of dealing with 3-D environments through self-organization and emergent behaviours, is proposed. Ant colonies, defined in terms of moving, pheromone laying, reproduction, death and deviating behaviours rules, is able to segment artificially generated objects of different shape, intensity, background. The model depends on few parameters and provides an elegant solution for the segmentation of 3-D structures in noisy environments with unknown range of image intensities: even when there is a partial overlap between the intensity and noise range, it provides a complete segmentation with negligible contamination (i.e., fraction of segmented voxels that do not belong to the object). The CAM is already in use for the automated detection of nodules in lung Computed Tomographies.

Tumor cells in cerebral glioma invade sur-rounding tissues preferentially along white matter tracts, spreading beyond the abnormal area seen on conventional Magnetic Resonance Images (MRI). Diffusion tensor imaging (DTI) can reveal larger peritumoral abnormalities in gliomas that are not ap-parent on MRI. Our aim was the characterization of pathological vs healthy tissue in DTI datasets by 3D statistical texture analysis, developing a semi automatic segmentation technique (CAD, Computer Assisted Detection sys-tem) for cerebral glioma, especially useful in patient follow-up during chemotherapy and for preoperative assessment of tumor extension.

Tumor cells in cerebral glioma invade the surrounding tissues preferentially along white-matter tracts, spreading beyond the abnormal area seen on conventional MR images. Diffusion Tensor Imaging can reveal large peritumoral abnormalities in gliomas, which are not apparent on MRI. Our aim was to characterize pathological vs. healthy tissue in DTI datasets by 3D statistical Texture Analysis, developing an automatic segmentation technique (CAD, Computer Assisted Detection) for cerebral glioma based on a supervised classifier (an artificial neural network). A Matlab GUI (Graphical User Interface) was created to help the physician in the assisted diagnosis process and to optimize interactivity with the segmentation system, especially for patient follow-up during chemotherapy, and for preoperative assessment of tumor extension. Preliminary tissue classification results were obtained for the p map (the calculated area under the ROC curve, AUC, was 0.96) and the FAmap (AUC¼0.98). Test images were automatically segmented by tissue classification; manual and automatic segmentations were compared, showing good concordance.

Gliomas are the most common primary brain tumors. The diffuse infiltration of white matter (WM) tracts by cerebral gliomas is a major cause of their appalling prognosis: tumor cells invade, displace, and possibly destroy WM. An early diagnosis and a comprehensive evaluation of tumor extent and relationships with surrounding ana- tomical structures are crucial in determining prognosis and treatment planning. Conventional MRI sequences (e.g. T1- or T2-weighted images) have limited sensitivity and specificity in diagnosing brain tumors [1], because they do not always allow precise delineation of tumor mar- gins, or tumor differentiation from edema and/or treatment effects. In particular, contrast-enhanced MR images may underestimate lesion margins, which is critical for image-guided tumor resection, radio- therapy planning, and for assessing the response to chemotherapy. On the contrary, Diffusion Tensor Imaging (DTI) can identify peritumoral white-matter abnormalities [2], by detecting the presence of small areas with tumor-cell infiltration in WM around the edge of the gross tumor, as confirmed by image guided biopsies. In particular the tumor core is characterized by reduced anisotropy and increased isotropy, while, around this area, tumor infiltration shows increased isotropy, but normal anisotropy [3]. The aims of this study were: (a) to characterize pathological and healthy tissue in DTI datasets by 3D statistical texture analysis; (b) to develop a (semi-automated) Computer Assisted Detection (CAD) system for cerebral tumors, remotely accessed over the Internet.

Purpose: Diffusion Tensor Imaging (DTI) can identify peritumoral white-matter abnormalities in cerebral gliomas not apparent on conventional MRI, that can be referred to infiltration regions surrounding the tumor core. Our aim was to characterize pathological and healthy tissue in DTI datasets by 3D statistical texture analysis, with the purpose of developing a semi-automated detection technique (CAD) of cerebral tumors. Methods and materials: Fifteen patients with gliomas (9 low-grade, 6 high-grade) were selected. 3T MRDTI consisted of a single-shot EPI sequence (b=1000 s/mm2, 32 gradient directions). Diffusion maps were obtained (anisotropy maps: FA and q; isotropy maps: MD and p). Manual segmentation of pathological areas was performed on each map; 3D texture analysis was applied to these ROIs and to the contralateral healthy tissue, in order to identify discriminating features based on cooccurrence and “run length” matrices. Ninety features were calculated, with a sliding-window approach; the most representative ones were selected by the Fisher filter, and Principal Component Analysis was applied, followed by Neural Network training. Results: Six patients were employed for training, nine for testing. Sensitivity, specificity and ROC curves were calculated, giving satisfactory results (95% sensitivity at 88% specificity, ROC AUC 0.89). Test images were automatically segmented by tissue classification; manual and automatic segmentations were compared by the Jaccard coefficient, and were in good accord. Mapping of Principal Components was used to characterize the tumoral structure. Conclusion: This semi-automated approach looks promising for preoperative assessment of structural heterogeneity and extension of cerebral gliomas and for evaluating response to chemotherapy.

The purpose of the work here presented consists in the evaluation of the performance of CAD (Computer Aided Detection) systems for automated lung nodule identification on multislice CT examinations based on different analysis approaches and on their combination. Three different CADe systems, the CAMCAD (Channeler Ant Model), the RGVPCAD (Region Growing Volume Plateau) and the VBNACAD (Voxel Based Neural Approach) were tested on public research datasets and evaluated in terms of FROC (Free-response Receiver Operating Characteristics) curves both individually and combined.

This work is a part of the MAGI5 (Medical Applications on a Grid Infrastructure Connection) experiment of the Italian INFN (Istituto Nazionale di Fisica Nucleare). A simple CAD (Computer Assisted Detection) system for juxta pleural lung nodules in CT images is presented, with the purpose of comparing different 2D concavity patching techniques and assessing the respective efficiency in locating nodules.

Purpose: The hippocampus, located in the medial temporal lobe (MTL), plays an essential role in learning and memory functions. Because of its frequent and early involvement in Alzheimer's disease (AD) and other neurodegenerative diseases, it is often targeted by both structural and functional imaging. Our aim is to increase the likelihood of early recognition and assessment of AD. Methods and Materials: We propose an approach that does not directly tackle the objective of hippocampal segmentation, but simply extracts from the right and left sides of a MR image two small fixed-size, parallelepiped-shaped sub images containing the hippocampi and adjacent structures (Hippocampal Boxes, HBs). We developed an automatic procedure for selecting an optimal number of HBs, starting from which we can extract both hippocampal formations in any MR image. We discriminate between controls and AD. This way MCI population can be evaluated and a prediction on the conversion to AD is made. Results: We extracted HBs from a set of 532 images from different sources, and developed a method to discriminate between converters and non converters to AD in a MCI population. The separation between Control and AD, measured as the area under the ROC curve, is 86,3%. For the Normal vs. Converted to AD cohorts, the area under the ROC curve is 88%. The forecast is checked against clinical follow up. Conclusion: The proposed approach consists in the possibility of automatically performing morphometric studies on the MTL. This procedure can quickly and reliably provide additional information in early AD diagnosis. The study has been established within the framework of the MAGIC-5 collaboration.

Gliomas are the most common primary brain tumors, with a typical infiltrative growth pattern along white matter (WM) fibers. Diffusion Tensor Imaging (DTI) is sensitive to the directional diffusion of water along WM tracts, which allows the identification of subtle peritumoral glioma infiltration that are not apparent on conventional Magnetic Resonance imaging. The aim of this study was to characterize pathological and healthy tissue in DTI datasets by statistical texture analysis, developing a Computer Assisted Detection (CAD) technique for cerebral glioma. This system, coupled to voxel-based tumor evolution analysis, could allow objective tumor identification and qualitative and quantitative measurements in the follow-up of patients during chemotherapy. In this paper, preliminary results of tumor segmentation and evolution analysis are shown.

Tumor cells in cerebral glioma invade surrounding tissues preferentially along white matter tracts, spreading beyond the abnormal area seen on conventional MR images. Diffusion tensor imaging can reveal larger peritumoral abnormalities in gliomas that are not apparent on MRI. Our aim was to characterize pathological vs healthy tissue in DTI datasets by 3D statistical Texture Analysis, developing an automatic segmentation technique (CAD) for cerebral glioma, especially useful in a patient follow-up during chemotherapy, and for preoperative assessment of tumor extension. Fifteen patients with glioma (9 low-grade, 6 high-grade) were selected. 3T MR-DTI consisted of a single-shot EPI sequence (b=1000 s/mm2, 32 gradient directions). Fractional anisotropy (FA), mean diffusivity (MD), p and q maps, were obtained. Manual segmentation of pathological areas was performed on each map. 3D texture analysis was applied with a sliding window approach to the segmented ROIs and to the contralateral healthy tissue, in order to identify discriminating features from the intensity and the gradient histogram, and from the cooccurrence (COM) and the run length matrix (RLM). After determining (according to their Fisher-filter score) the best features for each map, the feature-space dimensionality was reduced by Principal Component Analysis, and a neural-network classifier was trained. Glioma segmentations, performed by tissue classification, were compared with the manual ones. Six patients were employed for training, nine for testing. Classifier sensitivity, specificity and ROC curves were calculated: preliminary results were obtained for the p map (AUC = 0.96, sensitivity and specificity equal to 90%, classification error 10.0%) and FA map (AUC = 0.98, sensitivity and specificity equal to 92.6%, classification error equal to 7.3%). Test images were automatically segmented by tissue classification; manual and automatic segmentations were compared, showing good concordance. Our preliminary results show that this approach could allow objective tumor identification and quantitative measurement, with good accuracy.

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 preprocessing 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 partialvolume effects, while 3D morphology operations ensure the accurate inclusion of all the nodules (internal, pleural, and vascular) in the segmented volume. The newalgorithm was initially developed and tested on a dataset of 130 CT scans fromthe Italung-CT trial, andwas 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: themean overlap degree of the segmentationmasks 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.

COMPUTER ASSISTED DETECTION IN NEUROIMMAGINI FLAIR E DTI: INDIVIDUAZIONE, SEGMENTAZIONE AUTOMATICA E VOLUMETRIA DEI GLIOMI CEREBRALI Marina Donativi (1,6), Antonella Castellano (2,6), Giorgio De Nunzio (1,6), Gabriella Pastore (3,6), Matteo Rucco (4,6), Antonella Iadanza (2), Marco Riva (5), Lorenzo Bello (5), Andrea Falini (2) 1. Dip. di Matematica e Fisica “Ennio De Giorgi”, Univ. del Salento, Lecce 2. U.O. Neuroradiologia, Ospedale San Raffaele e Univ. Vita-Salute, Milano 3. Istituto di Ricerche Cliniche Ecomedica, Centro di Radioterapia e IGRT, Empoli 4. Univ. di Camerino, School of Science and Technology, Computer Science Division, Camerino 5. U.O. Neurochirurgia, Ist. Clinico Humanitas, Univ. di Milano, Milano 6. Advanced Data Analysis in Medicine, http://adamgroup.it FINALITA'. I sistemi automatici (CAD, Computer Assisted Detection) per la segmentazione e la volumetria dei gliomi cerebrali sono di grande interesse per la valutazione dell’estensione tumorale nella diagnosi, la pianificazione terapeutica e il follow-up: l’uso di un CAD può ridurre la soggettività della diagnosi, aumentandone l’accuratezza. In questo studio le immagini FLAIR e DTI di pazienti con glioma cerebrale sono state elaborate tramite analisi statistica tessiturale 3D per caratterizzare la struttura del tessuto tumorale e sviluppare un tool di segmentazione supervisionata automatica (“GlioCAD”), che fornisca la misura volumetrica delle lesioni e una valutazione degli istogrammi dei livelli di grigio di regioni di interesse [1]. MATERIALI E METODI. 34 pazienti con gliomi di basso e alto grado sono stati sottoposti a RM a 3T con sequenze 3D-FLAIR assiali e DTI (single-shot EPI, b=1000 s/mm2, 32 direzioni). Dalle immagini DTI sono state ottenute le mappe FA, MD, p e q. In ogni mappa sono state segmentate manualmente le regioni patologiche e, a queste ed al tessuto sano controlaterale, è stata applicata l’analisi tessiturale per identificare le caratteristiche (feature) discriminanti. La dimensionalità dello spazio delle feature è stata ridotta tramite Linear Discriminant Analysis (LDA), permettendo la classificazione e la segmentazione automatica del tumore. RISULTATI. Per ogni mappa sono state calcolate sensibilità, specificità e curve ROC, con ottimi risultati (0.90≤AUC≤0.97). Per la fruizione in remoto di GlioCAD è stato realizzato un plugin per OsiriX, che permette caricamento e visualizzazione delle immagini da segmentare, e l’invio al server sul quale risiede GlioCAD. Dopo un tempo di calcolo compatibile con la pratica clinica, le segmentazioni tornano al client e sono visualizzate insieme alle misure volumetriche e agli istogrammi. CONCLUSIONI. GlioCAD si propone come nuovo strumento, basato sull’analisi statistica tessiturale, per la segmentazione automatica dei gliomi cerebrali, la volumetria, e l’analisi quantitativa degli istogrammi nelle regioni di interesse. [1] G. De Nunzio, G. Pastore, M. Donativi, A. Castellano, A. Falini, A CAD system for cerebral gliomas based on texture features in DT-MR images, NIM A, doi:10.1016/j.nima.2010.12.086 (2011)

Computer-Assisted Detection in FLAIR and DT neuroimages: automatic segmentation and volume assessment of cerebral gliomas. Giorgio De Nunzio1,6, Marina Donativi1,6, Antonella Castellano2,6, Gabriella Pastore3,6, Matteo Rucco4,6, Antonella Iadanza2, Marco Riva5, Lorenzo Bello5, Andrea Falini2 (1) Univ. of Salento, Dept. of Mathematics and Physics, and INFN, Lecce (2) U.O. Neuroradiologia, Ospedale San Raffaele e Univ. Vita-Salute, Milano (3) Istituto di Ricerche Cliniche Ecomedica, Centro di Radioterapia e IGRT, Empoli (4) Univ. of Camerino, School of Science and Technology, Computer Science Division, Camerino (5) U.O. Neurochirurgia, Ist. Clinico Humanitas, Univ. di Milano, Milano (6) ADAM srl, Advanced Data Analysis in Medicine, http://adamgroup.it Purpose: tumor cells in cerebral gliomas invade surrounding tissues preferentially along WM tracts, spreading beyond the abnormal area depicted on conventional MR images. Diffusion Tensor Imaging can reveal larger peritumoral abnormalities in gliomas that are not apparent on conventional MRI. We aimed at characterizing pathological vs healthy tissue in FLAIR and DTI datasets by 3D statistical Texture Analysis, developing an automatic segmentation technique for cerebral glioma, hereafter called GlioCAD, especially useful in patient follow-up during chemotherapy, and for preoperative assessment of tumor extension. Methods and materials: thirty-four patients with gliomas were selected. 3T axial 3D-FLAIR, axial 3D-T1w, and DTI (single-shot EPI sequence, b=1000 s/mm2, 32 gradient directions) were acquired. Isotropic and anisotropic maps (FA, MD, p and q) were calculated, and pathological ROIs were manually drawn. 3D texture features were calculated with a sliding window approach in the segmented ROIs and in the contralateral healthy tissue, for CAD-system training. The feature-space dimensionality was reduced by Linear Discriminant Analysis, which allowed tissue classification by simple thresholding. Results: For each map, tumor-classification sensitivity, specificity and ROC curves (0.90≤AUC≤0.97) were calculated, and manual and automatic segmentations were compared by the Jaccard Coefficient, showing good concordance. The CAD system automatically calculated lesion volumes and histograms. With the purpose of allowing remote fruition of GlioCAD, a Graphical User Interface was designed as a plugin for OsiriX, a well-known radiological viewer. Conclusion: GlioCAD is proposed as a new tool, based on statistical textural analysis, for the automatic segmentation and volume assessment of brain gliomas, and for the quantitative analysis of the histograms in the regions of interest.

The characterization of tumoral tissue obtained by splitting the diffusion tensor into its isotropic (p) and anisotropic (q) components allows to reveal tumoral and peritumoral abnormalities in gliomas that are not apparent on conventional MR imaging and to detect the presence of microscopic tumor cells infiltration in white matter around the edge of the gross tumor, as confirmed by image guided biopsies [1]. The evaluation of this microscopic infiltration could also give better insights into the assessment of response to chemotherapy, as changes in pattern of water diffusion after successful or failed treatment could occur prior to alteration in size, thus reflecting a dynamic reorganization of the heterogeneous tumor structure during chemotherapy. Recently, changes in diffusion and perfusion metrics during treatment have been evaluated by parametric response maps, that allow a voxel-by-voxel comparison of measures over time with respect to a baseline map. This voxel-wise approach, when referred to the evaluation of ADC changes, is termed the functional diffusion maps (fDMs) [2,3]. In this study, we aim to evaluate diffusion tensor decomposition-derived metrics in a functional manner, by applying fDMs analysis to isotropic (p) and anisotropic (q) maps during neuroradiological follow-up of patients undergoing to dose-dense temozolomide chemotherapy before second-look surgery; changes in diffusion parameters within tumor tissue are correlated both with neurophysiological data from intraoperative subcortical mapping and histopathological findings from specimens obtained from image-guided tumor biopsies.

Objectives To explore the role of diffusion tensor imaging (DTI)-based histogram analysis and functional diffusion maps (fDMs) in evaluating structural changes of low-grade gliomas (LGGs) receiving temozolomide (TMZ) chemotherapy. Methods Twenty-one LGG patients underwent 3T-MR examinations before and after three and six cycles of dose-dense TMZ, including 3D-fluid-attenuated inversion recovery (FLAIR) sequences and DTI (b = 1000 s/mm2, 32 directions). Mean diffusivity (MD), fractional anisotropy (FA), and tensor-decomposition DTI maps (p and q) were obtained. Histogram and fDM analyses were performed on co-registered baseline and post-chemotherapy maps. DTI changes were compared with modifications of tumour area and volume [according to Response Assessment in Neuro-Oncology (RANO) criteria], and seizure response. Results After three cycles of TMZ, 20/21 patients were stable according to RANO criteria, but DTI changes were observed in all patients (Wilcoxon test, P ≤ 0.03). After six cycles, DTI changes were more pronounced (P ≤ 0.005). Seventy-five percent of patients had early seizure response with significant improvement of DTI values, maintaining stability on FLAIR. Early changes of the 25th percentiles of p and MD predicted final volume change (R2 = 0.614 and 0.561, P < 0.0005, respectively). TMZ-related changes were located mainly at tumour borders on p and MD fDMs. Conclusions DTI-based histogram and fDM analyses are useful techniques to evaluate the early effects of TMZ chemotherapy in LGG patients.

Objectives We detail a procedure for generating a set of templates for the hippocampal region in Magnetic Resonance images, representative of the clinical conditions of the population under investigation. Methods The first step is robust standardization of the intensity scale of brain MR images, belonging to patients with different degrees of neuropathology (Alzheimer’s Disease). So similar tissues have similar intensities, even across images coming from different sources. After the automatic extraction of the hippocampal region from a large dataset of images, we address template generation, choosing by clusterization methods a small number of the extracted regions. Results We assess that template generation is largely independent on the clusterization method and on the number and the clinical condition of the patients. The templates are chosen as the most representative images in a population. The estimation of the ‘minimum’ number of templates for the hippocampal region can be proposed, using a metric based on the geometrical position of the extracted regions. Conclusions This study describes a simple and easily reproducible procedure to generate templates for the hippocampal region. It can be generalized and applied to other brain regions, which may be relevant for neuroimaging studies.

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. METHODS: The first step is robust standardization of the intensity scale of brain MR images, belonging to patients with different degrees of neuropathology (Alzheimer's disease). So similar tissues have similar intensities, even across images coming from different sources. After the automatic extraction of the hippocampal region from a large dataset of images, we address template generation, choosing by clusterization methods a small number of the extracted regions. RESULTS: We assess that template generation is largely independent on the clusterization method and on the number and the clinical condition of the patients. The templates are chosen as the most representative images in a population. The estimation of the "minimum" number of templates for the hippocampal region can be proposed, using a metric based on the geometrical position of the extracted regions. CONCLUSIONS: This study describes a simple and easily reproducible procedure to generate templates for the hippocampal region. It can be generalized and applied to other brain regions, which may be relevant for neuroimaging studies.

Abstract. – Purpose: To investigate if early epidural analgesia can influence fetal head engage- ment into the pelvis and if it can increase the rate of transverse and asynclitic position during labour. Materials and Methods: 195 women with combined spinal-epidural analgesia (CSE) or with- out neuraxial analgesia were studied. CSE was performed using a mixture of ropivacaine 0.02% with 0.3 μg/ml of sufentanil administered in the spinal space. Maintenance of analgesia was man- aged with intermittent epidural administration of 10-15 ml of ropivacaine (0.07%-0.10%) mixed with 0.5 μg/ml of sufentanil, based on the stage of labour and the degree of pain. 2D transabdominal ultrasound (US) was used. Serial transabdominal US examinations were performed at 45-90 min in- tervals to detect transverse and asynclitic posi- tions, using the following signs: squint sign, sun- set thalamus and cerebellum signs that best de- tails the fetal head station. After delivery, the com- plete set of clinical and US data obtained by each examination were recorded and compared in women with and without labour analgesia. Data were examined by independent reviewers. Results: There was no difference in obstetric outcome between women in whom CSE had been used and those who did not request anal- gesia during labour (p>0.05). Conclusions: Epidural analgesia initiated early during labour and using low doses does not in- crease the rate of dystocic labors. Transverse fetal head positioning with anterior or posterior asyn- clitism does not seem to be promoted by drug or technique-related mechanisms, but rather should be the consequence of cephalopelvic disproportion.

Robotic Surgery is a current procedure in endosurgery, but literature has not addressed the learning curve for the use of the robotic assisted surgery. The learning curve for robot surgical procedures varies widely. Apart from innate skill, learning curves are composed of at least two fundamentals related to the volume of cases and the incidence rate. Commonly cited reasons include lack of adequate training in residency programs because of the time devoted to abdominal, vaginal, and obstetric procedures, lack of available and adequate training opportunities outside of dedicated fellowships, lack of proctors and mentor surgeons in communities to help to further advance the skills of younger surgeons, and lack of desire to leave established surgical practices to try to develop skills requiring long learning curves to master. Currently, the training involves practice with the surgical robot in either pig or human fresh tissue in a laboratory environment in order to become familiar with the functions of the robot, the attachment of the robotic arms to the robotic trocars, and the overall functions of the robotic console. In this chapter authors reviewed current literature on learning curve in robotic assisted surgery and screened problems linked to robotic surgical skills.

Conducting experiments in physics using modern measuring techniques, and particularly those utilizing computers, is often much more attractive to students than conducting experiments conventionally. However, the cost of professional kits in the Czech Republic is still very expensive for many schools. The basic equipment for one student workplace in the case of professional kits such as Vernier, Pasco or Coach costs around 800 euros. In this paper some physics experiments in which a computer, or a tablet with Microsoft Windows, is used as the measuring device, along with available physical devices such as a laser pointer, a solar cell or an electret microphone, are presented as suitable and alternative ways to carry out lab work. We show that it is possible to perform very simple school experiments (both as a central demonstration and as individual experimentation), in which high accuracy and clear final conclusions can be achieved at a very low cost. Further information is published on the specialized webpage www.sclpx.eu/ index.php?lang=en. The worksheets are in Czech, but the English version is in preparation.

Aptamers are chemically produced oligonucleotides, able to bind a variety of targets such as drugs, proteins and pathogens with high sensitivity and selectivity. Therefore, aptamers are largely employed for producing label-free biosensors (aptasensors), with significant applications in diagnostics and drug delivery. In particular, the anti-thrombin aptamers are biomolecules of high interest for clinical use, because of their ability to recognize and bind the thrombin enzyme. Among them, the DNA 15-mer aptamer (TBA), has been widely explored around the possibility of using it in aptasensors. This paper proposes a microscopic model of the electrical properties of TBA and of the aptamer-thrombin complex, combining information from both structure and function, following the issues addressed in an emerging branch of electronics known as proteotronics. The theoretical results are compared and validated with measurements reported in the literature. Finally, the model suggests resistance measurements as a novel tool for testing aptamer-target affinity.

Il trasferimento e l’integrazione di informazione fra discipline scientifiche molto diverse è sempre foriero di progresso. Questo seminario si propone di illustrare le caratteristiche della materia vivente, coniugando gli strumenti della fisica tradizionale con quelli della biologia e della chimica. Il fine è quello di aprire nuove strade, inventare nuove strategie e, nel far questo, da una parte si progredisce nella ricerca specifica, dall’altro si approda a visioni nuove, prospettive inesplorate che allargano gli orizzonti della scienza in generale.

The development of algorithms for the analysis of medical images has been progressively growing over the past two decades. The most common approach is the deployment of standalone workstations, equipped with provider-dependent Graphic User Interfaces (GUI) from which the algorithm execution is triggered interactively. There are, however, several drawbacks: among them, the GUI development cost, the GUI learning curve for the users, the high fixed cost of the software licenses, the difficulty in upgrading the software release. For a few years, the hypothesis of using Grid Services has been explored by several research groups. It turned out that there were other drawbacks: the high costs and security risks of integrating computing resources of medical centers into a Grid Computing Infrastructure. The emerging of Cloud computing, accessible via secure Web protocols, solves most - if not all - the problems. In the specific case of lung Computer Assisted Detection, a further important reason favors the SaaS (Software as a Service) approach: it was demonstrated by several works that combining CAD algorithms improves the overall performance. The system we present is composed by three main building blocks: WIDEN (Web-based Image and Diagnosis Exchange Network) handles the workflow, the image upload and the CAD result notification; the OpenNebula-based cloud IaaS (Infrastructure as a Service) batch farm allocates virtual computing and storage resources; the M5L CAD provides the nodule detection functionality. Our proposed implementation securely handles sensitive patient data, since images are transferred with the HTTPS protocol and the underlying virtual batch farm is isolated. Moreover it is efficient since it dynamically scales according to user requests thanks to the cloud backend.

Human olfactory 17-40 and Bacteriorhodopsin are two protein receptors that received particular attention in electronics, due to the possibility of implementing nano-biodevices able to detect odours and light and thus useful for medical and green energy harvesting applications. Some recent experiments concerning the electrical responses of these receptors are reviewed. Data are interpreted in the framework of a new science exploiting the complexity in biology and biomedical engineering called proteotronics. In particular, the single protein is modelled as an impedance network whose topological properties affect the electrical response as measured by experiments.

The paper is focused on a tiSsue-Based Standardization Technique (SBST) of magnetic resonance (MR) brain images. Magnetic Resonance Imaging intensities have no fixed tissue-specific numeric meaning, even within the same MRI protocol, for the same body region, or even for images of the same patient obtained on the same scanner in different moments. This affects postprocessing tasks such as automatic segmentation or unsupervised/supervised classification methods, which strictly depend on the observed image intensities, compromising the accuracy and efficiency of many image analyses algorithms. A large number of MR images from public databases, belonging to healthy people and to patients with different degrees of neurodegenerative pathology, were employed together with synthetic MRIs. Combining both histogram and tissue-specific intensity information, a correspondence is obtained for each tissue across images. The novelty consists of computing three standardizing transformations for the three main brain tissues, for each tissue class separately. In order to create a continuous intensity mapping, spline smoothing of the overall slightly discontinuous piecewise-linear intensity transformation is performed. The robustness of the technique is assessed in a post hoc manner, by verifying that automatic segmentation of images before and after standardization gives a high overlapping (Dice index >0.9) for each tissue class, even across images coming from different sources. Furthermore, SBST efficacy is tested by evaluating if and how much it increases intertissue discrimination and by assessing gaussianity of tissue gray-level distributions before and after standardization. Some quantitative comparisons to already existing different approaches available in the literature are performed.

Robust gray-level standardization in brain Magnetic-Resonance images. G. De Nunzio1, R. Cataldo1, A. Carlà1. (1) University of Salento, Dept. of Mathematics and Physics, and INFN, Lecce Purpose: it is known that intensities in MRI do not have a fixed tissue-specific numeric meaning, even within the same MRI protocol, for the same body region, or for images of the same patient obtained on the same scanner in different moments. Consequently many problems can arise in large multi-site clinical studies, making the interpretation of results difficult or confused, or affecting post processing phases such as segmentation and registration. In spite of the fact that the lack of a standard and quantifiable interpretation compromises the precision, accuracy, and efficiency of those applications, few papers have explicitly addressed the problems. In this context, we propose a tiSsue-Based Standardization Technique (SBST) of MR brain images. Methods and materials: the system was developed and tested on a large number of images, belonging to healthy people and to patients with different degrees of neurodegenerative pathology, obtained from public databases and the clinical practice. Both histogram and tissue-specific intensity information were used, performing piecewise linear intensity transformations between images, so sharing the simplicity and robustness of landmark techniques, while remaining fully automated and quite light from the computational point of view. Results: the efficacy in minimizing the risk of “mixing” brain tissues during intensity transformations was assessed, and particular attention was devoted to a thorough examination of the benefits comparing SBST with other approaches available in the literature. Conclusion: the technique proved robust in standardizing tissues, giving similar intensities to similar tissues, even across images coming from different sources.

Segmentazione di immagini FLAIR assiali basata su analisi topologica dei dati Matteo Rucco (4,6), Antonella Castellano (2,6), Marina Donativi (1,6), Giorgio De Nunzio (1,6), Emanuela Merelli(4), Damir Herman (7), Tanya Petrossian (7), Gabriella Pastore (3,6), Lorenzo Bello (5), Andrea Falini (2) 1. Dip. di Matematica e Fisica “Ennio De Giorgi”, Univ. del Salento, Lecce 2. U.O. Neuroradiologia, Ospedale San Raffaele e Univ. Vita-Salute, Milano 3. Istituto di Ricerche Cliniche Ecomedica, Centro di Radioterapia e IGRT, Empoli 4. Univ. di Camerino, School of Science and Technology, Computer Science Division, Camerino 5. U.O. Neurochirurgia, Ist. Clinico Humanitas, Univ. di Milano, Milano 6. Advanced Data Analysis in Medicine, http://adamgroup.it 7. Ayasdi Inc., Palo Alto - California FINALITA'.La necessitá di sviluppare tecniche innovative per l’analisi di big dataè stata in parte soddisfatta con lo sviluppo di algoritmi che trovano origine nella topologia algebrica e forniscono una rappresentazione compatta del dataset iniziale ed enfatizzano correlazioniche metodi statistici classici offuscherebbero. In questo studio abbiamo applicato l’algoritmo ”Mapper”[1] implementato nel software Iris che fornisce la rappresentazione in forma di un grafo costituito da nodi (cluster).La rappresentazione a grafo è ottenuta proiettando le coordinate dei pixel in uno spazio metrico, i nuovi pixel sono collegati fra loro per similaritá di toni di grigio di immagini FLAIR assiali. I cluster statisticamente significativi sono raggruppatiin strutture ad “Y”o circolarie rappresentano i tessuti potenzialmente patologici. MATERIALI E METODI.Lo spazio metrico adottato è quello di Hamming, esso è in grado di individuare cluster statisticamente significativi sia in pazienti con gliomi omogenei, sia in pazienti con strutture tumorali disomogenee. Per ogni paziente sono state estratte 4 slice dal volume patologico: 2 corrispondenti alle facce superiori inferiori e 2 centrali scelte casualmente. RISULTATI. Per ogni slice analizzata sono stati selezionati i pixel che costituivano i cluster individuati da Iris. I sottinsiemi dei pixel sono stati proiettati sulle immagini originali tracciando delle ROI.Queste maschere sono state confrontate con quelle prodotte dal medico, la qualitá del sistema di segmentazione è stata valutata con il coefficiente di Jaccard, i risultati sono sicuramente incoraggianti (valore medio di Jaccard pari 0.78) che potrebbe essere migliorato eliminando i pochi falsi positivi presenti con tecniche di morfologia matematica o con metodiche a soglia. CONCLUSIONI. Questo approccio basato esclusicavamente su toni di grigio si propone come nuovo strumento per la segmentazioni di immagini Flair. È in atto una sua validazione estensiva su 20 pazienti e sará testato su altre tipologie di immagini diagnostiche e/o su un set piu’ampio di feature. [1] Singh, Gurjeet, Facundo Mémoli, and Gunnar E. Carlsson. "Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition." SPBG. 2007.

Semantic segmentation of 3D models of ancient and historic buildings is an important modern Cultural Heritage topic. This work reports our preliminary results on the creation of a software system for partial semiautomatic semantic segmentation of building 3D models produced by photogrammetric surveys, and their fruition by Web technologies. These results were obtained in collaboration with Corvallis SPA (Padua – Italy, http://www.corvallis.it).

The diffuse and infiltrative growth of cerebral gliomas is a major determinant of poor prognosis. Tumor cells invade surrounding tissues preferentially along white matter tracts, spreading beyond the abnormal area seen on conventional MR images. The detection and characterization of this microscopic infiltration in a non-invasive manner is of outstanding importance for surgical and radiation therapy planning or to assess response to chemotherapy. Diffusion tensor imaging can reveal larger peritumoral abnormalities in gliomas that are not apparent on conventional MR imaging, by detecting the presence of microscopic tumor cells infiltration in white matter around the edge of the gross tumor, as confirmed by image guided biopsies. The aims of this study are: 1) to characterize pathological and healthy tissue in DTI datasets by 3D statistical Texture Analysis, developing a semi-automated segmentation technique of cerebral tumors; 2) to correlate segmentation results with histopathological findings from specimens obtained from image-guided tumor biopsies.

During the radiation therapy (RT) process, the treatment is planned and simulated with a treatment planning system (TPS). Contouring identifies the Planning Treatment Volume (PTV), that is the physical RT treatment volume. PTV of Glioblastoma (GB) includes, after expansion, Gross Tumor Volume (GTV, the tumor) and Clinical Target Volume (CTV, tumor plus edema). GlioCAD, a Computer-Assisted Detection software for contouring gliomas in MRI/DTI, was used to delineate GTV. The dataset included the images of 21 patients undergoing RT for GB. For each patient, we co-registered CT-planning images and diagnostic MRI (16 T1-gad, 6 T2 Flair, 13 Flair Fat Sat), which were used for GlioCAD training and validation. CAD outlined the tumor with good accuracy, after ruling out in post-processing some false positives. We identified reliable GTVs, suitable for RT requirements. An evolution of GlioCAD will take into account edema for outlining CTV. The method is promising. Together with a further automatic system for the delineation of organs at risk (OAR) in the brain, the procedure may be helpful for standardization of RT-treatment planning.

Le applicazioni della Fisica e dell’Informatica alla Biomedicina includono i sistemi di individuazione di patologie (CAD, Computer-Assisted Detection) basati sul trattamento di dati provenienti da esami diagnostici (in particolare ma non limitandosi alle immagini diagnostiche quali TC, RM, etc.), gli strumenti di ausilio alla chirurgia (realtà virtuale, telechirurgia), l’analisi e l’interpretazione di segnali di interesse biomedicale (per esempio segnali da elettroencefalogramma, EEG, o da elettrocardiogramma, ECG). Questo lavoro presenta una rassegna di applicazioni, in cui gli autori sono impegnati, dandone alcuni dettagli implementativi e discutendone brevemente i risultati. Le applicazioni si differenziano per il tipo di dati analizzati (serie temporali provenienti da misure EEG, oppure dati bi- o tridimensionali contenuti in immagini diagnostiche), per il distretto corporeo di intervento, le finalità, la patologia.

Purpose: To evaluate the safety and feasibility of supra-pubic percutaneous sclero-embolization (SE) in the treatment of symptomatic female pelvic varicocele (FPV), performed under local anesthesia. Materials and Methods: The authors selected 28 patients screened by transabdominal and transvaginal ultrasound, with venous Doppler signal. Clinicians performed SE by transfemoral catheterization, under local anesthesia, using of a mix of 2 ml of lauromacrogol 400 (Atossisclerol 3%, Chemische F. Kreussler, Wiesbaden, Germany) and 2 ml of air, in a mixed foam fashion. Results: The total operative time for SE was 7.6±2.1 min. Intra-surgical blood loss was 40±14 ml. No migration of sclerosant material occurred and postoperative analgesic request during a 48 hr period occurred in 6 patients. Technical success was 100%. The authors embolized 8 women bilaterally (28.5%), 18 on the left ovarian vein (OV) (64.2%) and 2 only in the right OV (7.1%): 7 women complained of transitory flank pain (25%), which disappeared in few minutes. The major complications in 10 days after SE were: fever (>38°C for two days) in 2 patients (7.1%) and pelvic pain for 3 days in eight patients (28.5%). After 30 days only 6 women suffered of FPV lower symptoms which disappeared in 180 days. A substantial reduction in size of pelvic varicosities was noted in all patients. Conclusions: SE is a safe and feasible procedure. It reduces significantly the mean time of scopies, the intensity of radiation emission, and it is performed under local anaesthesia. This minimally invasive procedure could be proposed to all women with supra-pubic FPV for its reproducibility and feasibility.

Laparoscopy is the standard of treatment for many gynecological diseases, it is a very common procedure in gynaecology and it is widely accepted as the method of first choice for many gynaecological problems. A meta-analysis of 27 randomized controlled trials comparing laparoscopy and laparotomy for benign gynaecological procedures concluded that the risk of minor complications after gynaecological surgery is 40% lower with laparoscopy than with laparotomy, although the risk of major complications is similar. Laparoscopy has been considered a real alternative to laparotomy with numerous advantages: short hospital stay, less need of analgesia, low intraoperative blood loss and faster recovery time. Many researchers are in pursuit of new technologies and new tools of minimally invasive technologies for reducing laparoscopic complications. The industry responded to these demands with many innovations, such as new optical instruments and digital images, virtual and augmented reality, robotic assisted surgery, etc. In this chapter, authors discussed the possible utilization of novel technologies to reduce the risk of laparoscopic gynecological complications.

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