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GT1 Data Analysis and Knowledge Discovery

Sameer Antani
U.S. National Library of Medicine / NIH (USA)

Agma J. M. Traina
University of Sao Paulo (BR)

Conference room

Amphitheater I

Session time

Thursday, June 22, 2017 - 14:00 to 15:30

21 Presentations

A Tool for Optimizing De-Identified Health Data for Use in Statistical Classification

Abstract:

When individual-level health data is shared in biomedical research the privacy of patients and probands must be protected. This is typically achieved with methods of data de-identification, which transform data in such a way that formal guarantees about the degree of protection from re-identification can be provided. In the process it is important to minimize loss of information to ensure that the resulting data is useful. A typical use case is the creation of predictive models for knowledge discovery and decision support, e.g. to infer diagnoses or to predict outcomes of therapies.

Fabian Prasser
TUM (DE)

Johanna Eicher

Raffael Bild

Helmut Spengler

Klaus Kuhn

A Recall Analysis of Core Word Lists over Children’s Utterances for Augmentative and Alternative Communication

Abstract:

The vocabulary definition is of paramount importance in the customization of AAC devices, and it can be based on core word lists proposals. However, despite having the same purpose, there is no consensus among these core word lists. Therefore, in order to present evidence that helps to decide which list has a better recall, in this paper, 9 core word lists for children were reviewed; in addition, a Super List by merging these 9 lists was made.

Natalia Franco
Federal University of Pernambuco (BR)

Augusto Lazzarotto Lima

Thiago Pinheiro Lima

Edson Alves Silva

Rinaldo José Lima

Robson Fidalgo

Computational Analysis of BRCA1 Mutations in Pediatric Patients with Malignancies and Their Mothers

Abstract:

Breast and ovarian cancers are the most prevalent amongst women. Similar incidence appear in childhood malignancies, where the basic ontogenetic mechanisms still remain to be elucidated. Such approaches, of relating mother’s cancer mutations with the prevalence of childhood cancer in their offspring could prove useful in the prognosis, early detection and therapy of childhood malignancies. The aim of the present study was to use computational and bioinformatics tools to investigate the incidence of mutations in mothers with children suffering from neoplasms.

George Lambrou
National Technical University of Athens (GR)

Ioanna Barbounaki

Fotini Tzortzatou-Stathopoulou

Ourania Petropoulou

Panagiotis Katrakazas

Dimitra Iliopoulou

Dimitrios Koutsouris
National Technical University of Athens (GR)

Multi-Label Modality Classification for Figures in Biomedical Literature

Abstract:

The figures found in biomedical literature are a vital part of biomedical research, education and clinical decision. The multitude of their modalities and the lack of corresponding meta-data, constitute search and information retrieval a difficult task. We present multi-label modality classification approaches for biomedical figures. In particular, we investigate using both simple and compound figures for training a multi-label model to be used for annotating either all figures, or only those predicted as compound by an initial compound figure detection model.

Athanasios Lagopoulos
Aristotle University of Thessaloniki (GR)

Anestis Fachantidis

Grigorios Tsoumakas

Personalization of Infectious Disease Risk Prediction: Towards Automatic Generation of a Bayesian Network

Abstract:

Infectious diseases have been a major cause of human morbidity, but most are avoidable. A relevant and accurate risk prediction is expected to alert people to the risk of getting exposed to infectious diseases. However, current approaches are limited to the contexts and static risk prediction model. Thus, a dynamic and growing prediction model, based on Bayesian Network (BN), is proposed to overcome these limitations.

Retno Vinarti

Lucy Hederman
Trinity College Dublin (IE)

BREATH: Heat Maps Assisting the Detection of Abnormal Lung Regions in CT Scans

Abstract:

Computed Tomography (CT) scans are often employed to diagnose lung diseases, as abnormal tissue regions may indicate whether proper treatment is required. However, detecting specific regions containing abnormalities in a CT scan demands time and effort of specialists. Moreover, different parts of a single lung image may present both normal and abnormal characteristics, what makes inaccurate the classification of a single lung as healthy (normal) or not.

Mirela T. Cazzolato
University of Sao Paulo - ICMC (BR)

Lucas C. Scabora

Alceu F. Costa

Marcos R. Nesso-Jr

Luis F. Milano-Oliveira

Daniel S. Kaster

Caetano Traina-Jr
University of Sao Paulo (BR)

Agma J. M. Traina
University of Sao Paulo (BR)

GT2 Decision Support Systems and Methods

Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)

Spiros Denaxas
University College London (UK)

Conference room

Amphitheater I

Session time

Thursday, June 22, 2017 - 16:00 to 17:15

20 Presentations

EEG Signal Analysis of Real-word Reading and Nonsense-word Reading between Adults with Dyslexia and without Dyslexia

Abstract:

The evolution in technology plays a major role in improving diagnostic accuracies. Pattern recognition and classification are techniques that may help uncover answers that are not always obvious. This paper attempts to discover such patterns found in brain wave signals in people with dyslexia using classifiers. Electroencephalogram (EEG) signals captured during real-word and nonsense-word reading activities from individuals with dyslexia are compared with normal controls.

Harshani Perera
Murdoch University (AU)

Mohd Fairuz Shiratuddin

Kok Wai Wong

Kelly Fullarton

Emotional state recognition using advanced machine learning techniques on EEG data

Abstract:

This study investigates the discrimination between calm, exciting positive and exciting negative emotional states using EEG signals. Towards this direction, a publicly available dataset from eNTERFACE Workshop 2006 was used having as stimuli emotionally evocative images. At first, EEG features were extracted based on literature review. Then, a computational framework is proposed using machine learning techniques, performing feature selection and classification into two at a time emotional states.

Katerina Giannakaki
University of Crete (GR)

Giorgos Giannakakis

Christina Farmaki

Vangelis Sakkalis

Estimation of Heart Failure Patients Medication Adherence through the Utilization of Saliva and Breath Biomarkers and Data Mining Techniques

Abstract:

The aim of this work is to estimate the medication adherence of patients with heart failure through the application of a data mining approach on a dataset including information from saliva and breath biomarkers. The method consists of two stages. In the first stage, a model for the estimation of adherence risk of a patient, exploiting anamnestic and instrumental data, is applied.

Evanthia Tripoliti

Theofilos Papadopoulos

Georgia Karanasiou
FORTH (GR)

Fanis Kalatzis

Yorgos Goletsis

Aris Bechlioulis

Silivia Ghimenti

Tommaso Lomonaco

Francesca Bellagambi

Roger Fuoco

Mario Marzilli

Maria Chiara Scali

Katerina Naka

Abdelhamid Errachid

Dimitris Fotiadis
University of Ioannina (GR)

Exploiting active microRNA interactions for diagnosis from expression profiling experiments

Abstract:

In silico diagnosis through microRNA expression profiling experiments is a promising direction in the clinical practices of bioinformatics science. The task is computationally defined as a classification problem where a query experiment is required to be assigned into one of the predefined diseases using a learned model from previously labeled samples. While several powerful machine learning models exist to perform this task, the challenging issue is how to feed these models by effectively encoded samples. This encoding requires a sensible representation of experiment content.

Erdem Corapcioglu

Hasan Ogul
Baskent University (TR)

The Effect of Mammogram Preprocessing on Microcalcification Detection with Convolutional Neural Networks

Abstract:

"Microcalcifications are an early mammographic indicator of breast cancer. To assist screening radiologists in reading mammograms, machine learning techniques have been developed for the automated detection of microcalcifications. In the last few years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision and medical image analysis applications. A key step in CNN-based detection is image preprocessing, including brightness and contrast variations.

Agnese Marchesi
University of Cassino and Southern Latium (IT)

Alessandro Bria

Claudio Marrocco

Mario Molinara

Francesco Tortorella
University of Cassino and South Lazio (IT)

Jan-Jurre Mordang

Nico Karssemeijer

GT3 Decision Support and Recommendation Systems

Alexander Astaras
American College of Thessaloniki (GR)

Daniel Sonntag
German Research Center for AI (DE)

Conference room

Amphitheater I

Session time

Friday, June 23, 2017 - 09:00 to 10:30

21 Presentations

Prognosis of abdominal aortic aneurysms: A machine learning-enabled approach merging clinical, morphometric, biomechanical and texture information

Abstract:

An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac.

Fernando Garcia-Garcia
ARTORG Center & University of Bern (CH)

Eleni Metaxa

Stergios Christodoulidis

Marios Anthimopoulos

Nikolaos Kontopodis

Martina Correa-Londono

Thomas R. Wyss

Yannis Papaharilaou

Christos V. Ioannou

Hendrik von Tengg-Kobligk

Stavroula Mougiakakou

A non-invasive medical decision support prototype system for Dermatology based on electrical impedance spectroscopy (DermaSense)

Abstract:

Premature detection of malignant melanoma remains the primary factor in reducing mortality from this form of cancer. During the last decade diagnostic sensitivity and specificity have improved through the utilization of new computer-based technologies, which help improve lesion selection for pathology review and biopsy. Despite these advances in melanoma diagnosis, initial detection, timely recognition and quick treatment of melanoma remain crucial.

Alexander Zogkas

Sotiria Gilou
Aristotle University of Thessaloniki (GR)

Inessa Kirsanidou

Chrysovalantis Korfitis

Christina Kemanetzi

Elizabeth Lazaridou

Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

Alexander Astaras
American College of Thessaloniki (GR)

Is a Decision Support System Based on Robson's Classification Enough to Reduce Cesarean Section Rates?

Abstract:

The cesarean section (CS) rates are important global indicators for measuring the access to obstetric services. In 2001, Robson proposed a CS classification in ten-groups as the most appropriate to compare surgery rates. However, having a decisional support system from Robson's Classification is enough to reduce CS rates? The births analysis that occurred in 2016, inside a public hospital maternity, showed 1,946 deliveries of which 35.7% were CS with a positive growth trend (R2 = 0.137).

Juliano Gaspar
Universidade Federal de Minas Gerais (BR)

Zilma Reis

Juliana Barra

Design and Development of a Mobile Decision Support System: Guiding Clinicians Regarding Law in the Practice of Psychiatry in Emergency Department

Abstract:

Decision-making in an emergency department needs to be efficient. It does not allow observation of the patient for a prolonged period of time, especially if the patients harm themselves or others, or refuses treatment. This includes suicidal, violent, intentional self-inflicted or non-consenting to treatments’ patient. Clinicians have to quickly decide whether to call the police, admit the patient to the psychiatric ward, according to recommended, predefined procedures.

Soudabeh Khodambashi
Norwegian University of Science and Technology (NO)

Florentin Moser

Jon Atle Gulla

Pekka Abrahamsson

Integrated decision support by combining textual information extraction, facetted search and information visualisation

Abstract:

This work focusses on our integration steps of complex and partly unstructured medical data into a clinical research database with subsequent decision support. Our main application is an integrated facetted search tool, followed by information visualisation based on automatic information extraction results from textual documents. We describe the details of our technical architecture (open-source tools), to be replicated at other universities, research institutes, or hospitals.

Daniel Sonntag
German Research Center for AI (DE)

Hans-Jurgen Profitlich

Predicting Sepsis Biomarker Progression under Therapy

Abstract:

Sepsis is a serious, life-threatening condition that presents a growing problem in medicine and health-care. It is characterized by quick progression and high variability in the disease manifestation, so treatment should be personalized and tailored to fit individual characteristics of a particular subject. That requires close monitoring of the patient's state and reliable predictions of how the targeted therapy will affect sepsis progression over time.

Ivan Stojkovic
Temple University (USA)

Zoran Obradovic
Temple University (USA)

GT4 Technology Enhanced Medical Education and Simulation

Iraklis Paraskakis
SEERC (GR) & The University of Sheffield (UK)

Meni Tsitouridou
Aristotle University of Thessaloniki (GR)

Conference room

Amphitheater I

Session time

Friday, June 23, 2017 - 14:00 to 15:30

21 Presentations

Design and Evaluation of a Virtual Reality Simulation Module for Training Advanced Temporal Bone Surgery

Abstract:

Surgical education has traditionally relied on cadaveric dissection and supervised training in the operating theatre. However, both these forms of training have become inefficient due to issues such as scarcity of cadavers and competing priorities taking up surgeons' time. Within this context, computer-based simulations such as virtual reality have gained popularity as supplemental modes of training.

Sudanthi Wijewickrema
University of Melbourne (AU)

Bridget Copson

Yun Zhou

Xingjun Ma
The University of Melbourne (AU)

Robert Briggs

James Bailey

Gregor Kennedy

Stephen OLeary

Inductive learning of the surgical workflow model through video annotations

Abstract:

Surgical workflow modeling is becoming increasingly useful to train surgical residents for complex surgical procedures. Rule-based surgical workflows have shown to be useful to create context-aware systems. However, manually constructing production rules is a time-intensive and laborious task. With the expansion of new technologies, large video archive can be created and annotated exploiting and storing the expert’s knowledge.

Hirenkumar Nakawala
Politecnico di Milano (IT)

Elena De Momi

Laura Erica Pescatori

Anna Morelli

Giancarlo Ferrigno

LiveBook: Competence Assessment with Virtual-Patient Simulations

Abstract:

Virtual-patient simulators play an important role in modern medical education. These simulators provide a safe environment for learning, give contextual feedback to learners, and allow the learner to move beyond the time and space constraints of traditional face-to-face medical instruction. In this paper, we present an interactive simulation system, LiveBook. This system interacts with students in natural language, and provides detailed feedback on the student's performance after a case has been studied.

Sina Jalali

Eleni Stroulia
University of Alberta (CA)

Sarah Foster

Sarah Forgie

Amit Persad

Diya Shi

Implementation of process-oriented feedback in a clinical reasoning tool for virtual patients

Abstract:

Virtual patients (VPs) offer a safe environment to teach clinical reasoning skills, but feedback is often provided in outcome-, rather than process-oriented fashion. For complex cognitive skills, such as clinical reasoning, the process itself is often more important then the end result, especially during learning phase. We have developed a tool that can be integrated into VP systems to specifically support the clinical reasoning process and provide both, outcome- and process-oriented feedback.

Inga Hege
Ludwig-Maximilians-Universität München (DE)

Andrzej A Kononowicz

Michal Nowakowski

Martin Adler

Novel Method for Storyboarding Biomedical Videos for Medical Informatics

Abstract:

We propose a novel method for developing static storyboard for video clips included with biomedical research literature. The technique uses both visual and audio content in the video to select candidate key frames for the storyboard. From the visual channel, the intra-frames are extracted using FFmpeg tool. IBM Watson speech-to-text service is used to extract words from the audio channel, from which clinically significant concepts (key concepts) are identified using the U.S. National Library of Medicine‘s Repository for Informed Decision Making (RIDEM) service.

Sema Candemir

Sameer Antani
U.S. National Library of Medicine / NIH (USA)

Zhiyun Xue

George Thoma

A Proposed Learner Activity Taxonomy and a Framework for Analysing Learner Engagement versus Performance using Big Educational Data

Abstract:

The inclusion of information and communication technologies in Healthcare and Medical Education is a fact nowadays. Furthermore numerous virtual learning environments have been established in order to host both educational material and learner’s online activities. Online modules in a VLE can be designed in very different ways being part of different types of courses, while different models can be used to design the course based on what the creator aims to achieve. Thus, the types and the importance of the different elements of the online course may vary a lot.

Stathis Th. Konstantinidis
University of Nottingham (UK)

Aaron Fecowycz

Kirstie Coolin

Heather Wharrad

George Konstantinidis

Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

GT5 Biomedical Signal

Behnaz Ghoraani
Florida Atlantic University (USA)

Christos Frantzidis
Aristotle University of Thessaloniki (GR)

Conference room

Amphitheater II

Session time

Friday, June 23, 2017 - 14:00 to 15:30

21 Presentations

Towards multi-parametric hub scoring of functional cortical brain networks: An electroencephalographic (EEG) study across lifespan

Abstract:

The attractiveness and robustness of graph theory has stimulated an unprecedented increase in studies aiming to understand the functional organization and dynamics of the brain. The investigation of brain connectomics produces a tremendous amount of data which may be examined at both a macroscopic and microscopic level. However, the interpretation of findings is still challenging. Novel methodological approaches should enhance the arsenal of the tools employed towards the understanding of the interaction of the distinct brain components.

Vasiliki Zilidou
Aristotle University of Thessaloniki (GR)

Christos Frantzidis
Aristotle University of Thessaloniki (GR)

Ana Vivas

Maria Karagianni

Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

Discrimination of Preictal and Interictal Brain States from Long-Term EEG Data

Abstract:

Discriminating the preictal state in EEG signals is of great importance in neuroscience and the epileptic seizure prediction field has yet to provide conclusive evidence. In this study, three different classification approaches, including the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm, Support Vector Machines (SVMs) and Neural Networks (NNs), are investigated for their ability to discriminate preictal from interictal EEG segments. Using public EEG data, a wide range of features is extracted from each segment and then applied to the classifiers.

Kostas M. Tsiouris
National Technical University of Athens (GR)

Vasileios C. Pezoulas

Dimitrios Koutsouris
National Technical University of Athens (GR)

Dimitris Fotiadis
University of Ioannina (GR)

Michalis Zervakis

Detection of Alcoholism based on EEG Signals and Functional Brain Network Features Extraction

Abstract:

Alcohol abuse disorder or alcoholism is a common disorder that leads to brain defects and associated cognitive, emotional and behavioural impairments. Finding and extracting discriminative biological markers, which are correlated to healthy brain pattern and alcoholic brain pattern, helps us to utilize automatic methods for detecting and classifying alcoholism. Many brain disorders could be detected by analyzing the Electroencephalography (EEG) signals. In this paper, for extracting the required markers we analyze the EEG signals for two groups of alcoholic and control subjects.

Negar Ahmadi

Yulong Pei
Technische Universiteit Eindhoven (NL)

Mykola Pechenizkiy

EEG Classification and Short-Term Epilepsy Prognosis using Brain Computer Interface Software

Abstract:

The recent advances of Brain Computer Interfaces (BCI) systems, can provide effective assistance for real time prognosis systems for patients who suffered from epileptic seizures. This paper presents an EEG classification strategy for short-term epilepsy prognosis, using software for Brain-Computer Interface (BCI) systems. A training scenario is presented, where significant features are extracted and a classification algorithm is trained. The training procedure extracts knowledge in terms of a classification model, which is employed in a real-time testing.

Alexandros Tzallas
Technological Educational Institute of Epirus (GR)

Nikolaos Giannakeas

Konstantinos Zoulis

Markos Tsipouras

Euripidis Glavas
Technological Educational Institute of Epirus (GR)

Katerina D. Tzimourta

Loukas G. Astrakas

Spyridon Konitsiotis

An Intermixed Color Paradigm for P300 Spellers: A Comparison with Gray-scale Spellers

Abstract:

P300 speller systems represent one of the most basic applications of Brain-Computer Interfaces (BCIs). A traditional P300 speller consists of a 6 by 6 grid of characters in which each column or row in this grid intensifies at random. During such intensification process, the electroencephalography (EEG) data of the subject is recorded and analyzed to determine the character to be spelled.

Mina Meshriky
Ain Shams University (EG)

Seif Eldawlatly

Gamal Aly

Simulation of Spiral Waves and Point Sources in Atrial Fibrillation with Application to Rotor Localization

Abstract:

Cardiac simulations play an important role in studies involving understanding and investigating the mechanisms of cardiac arrhythmias. Today, studies of arrhythmogenesis and maintenance are largely being performed by creating simulations of a particular arrhythmia with high accuracy comparable to the results of clinical experiments. Atrial fibrillation (AF), the most common arrhythmia in the United States and many other parts of the world, is one of the major field where simulation and modeling is largely used.

Prasanth Ganesan

Kristina Shillieto

Behnaz Ghoraani
Florida Atlantic University (USA)

GT6: eHealth studies

Carolyn Mcgregor
University of Ontario (CA)

Bridget Kane
Karlstad University Business School (SE)

Conference room

Amphitheater II

Session time

Friday, June 23, 2017 - 16:45 to 18:00

19 Presentations

Comparing the Quality of Numeracy Assessment Methods in Healthcare

Abstract:

Numeracy skill level of patients has great influence on their preferences and priorities for the treatment options concerning their healthcare. There have been different methods for assessment of numeracy skill in healthcare domain. In our previous research we proposed a new Confidence-based Patient Numeracy Assessment (C-PNA) method. In this paper we compare it with other numeracy assessment methods in terms of newly proposed quality characteristics.

Mandana Omidbakhsh

Olga Ormandjieva
Concordia University

Pattern-Based Statechart Modeling Approach for Medical Best Practice Guidelines - A Case Study

Abstract:

Improving effectiveness and safety of patient care is an ultimate objective for medical cyber-physical systems. Many medical best practice guidelines exist in the format of hospital handbooks which are often lengthy and difficult for medical staff to remember and apply clinically. Statechart is an effective tool to model medical guidelines and enables clinical validation with medical staffs. However, some advanced statechart elements could result in high cost, such as low understandability, high difficulty in clinical validation, formal verification, and failure trace back.

Chunhui Guo
Illinois Institute of Technology (USA)

Zhicheng Fu

Shangping Ren

Yu Jiang

Maryam Rahmaniheris

Lui Sha

Trust, Ethics and Access: Challenges in studying the work of Multidisciplinary Medical Teams

Abstract:

This paper highlights the challenges for researchers when undertaking research on multidisciplinary medical teams (MDTs) in real-world healthcare settings, and suggests ways in which these challenges may be addressed.

Bridget Kane
Karlstad University Business School (SE)

Saturnino Luz
University of Edinburgh (UK)

Using Affective Computing to automatically adapt serious games for rehabilitation

Abstract:

Although many studies investigate the automatic adaptation in serious games with the goal to improve the user's motivation, the most part of Affective Computing approaches requires a high development cost and usually does not consider an intervention of health professionals in the control of adaptations that will be executed in a game. This paper describes an approach to enable affective adaptation in serious games for motor rehabilitation with the involvement of physiotherapists.

Renan Vinicius Aranha
University of Sao Paulo (BR)

Leonardo Souza Silva

Marcos Lordello Chaim

Fatima De Lourdes Dos Santos Nunes

GT7 Medical Imaging I

Christos P Loizou
Cyprus University of Technology (CY)

Daniela Giordano
Universita Degli Studi di Catania (IT)

Conference room

Amphitheater I

Session time

Saturday, June 24, 2017 - 11:30 to 12:30

19 Presentations

Illumination correction by dehazing for retinal vessel segmentation

Abstract:

"Assessment of retinal vessels is fundamental for the diagnosis of many disorders such as heart diseases, diabetes and hypertension. The imaging of retina using advanced fundus camera has become a standard in computer-assisted diagnosis of opthalmic disorders. Modern cameras produce high quality color digital images, but during the acquisition process the light reflected by the retinal surface generates a luminosity and contrast variation.

Benedetta Savelli

Alessandro Bria

Claudio Marrocco

Mario Molinara

Francesco Tortorella
University of Cassino and South Lazio (IT)

Adrian Galdran

Aurelio Campilho

A differential geometry approach for change detection in medical images.

Abstract:

Change detection is of paramount importance in medical imaging, serving as a non-invasive quantifiable powerful tool in diagnosis and in assessment of the outcome of treatment of tumors. We present a new quantitative method for detecting changes in volumetric medical data and in clustering of anatomical structures, based on assessment of volumetric distortions that are required in order to deform a test three-dimensional medical dataset segment onto its previously-acquired reference, or a given prototype in the case clustering.

Alexander Naitsat
Technion – Israel Institute of Technology (IL)

Emil Saucan

Yehoshua Zeevi

Brain Image and Lesions Registration and 3D Reconstruction in Dicom MRI Images

Abstract:

During a human brain MRI acquisition the resulting image is formed out of 2D slices. The slices must then be aligned and reconstructed to provide a 3-dimensional (3D) visualization of the brain volume. We propose in this work, an integrated system for the register ion and 3D reconstruction of DICOM MRI images and lesions of the brain acquired from multiple sclerosis (MS) subjects at two different time intervals (time 0 (T0) and time 1 (T1)). The system facilitates the follow up of the MS disease development and will aid the doctor to accurately manage the follow up of the disease.

Christos P Loizou
Cyprus University of Technology (CY)

Christos Papacharalambous

Giorgos Samaras

Efthivoulos Kyriakou
Frederick University (CY)

Takis Kasparis
Cyprus University of Technology (CY)

Marios Pantziaris

Eleni Eracleous

Constantinos Pattichis
University of Cyprus (CY)

An Eye Tracker–based Computer System to Support Oculomotor and Attention Deficit Investigations

Abstract:

Eye tracking is a non-invasive procedure to acquire eye-gaze data. The accuracy offered by the new eye tracking technologies gives to physicians and scientists a great opportunity to employ eye trackers to perform quantitative assessment of eye movements for diagnostic and rehabilitation purposes. However, eye trackers do not support physicians in their analysis, as they typically lack specific software solutions tailored to the diseases under investigation.

Daniela Giordano
Universita Degli Studi di Catania (IT)

Carmelo Pino

Isaak Kavasidis

Concetto Spampinato

Massimo Di Pietro

Renata Rizzo

Anna Scuderi

Rita Barone

GT8: Ask the Editor

Pedro Pereira Rodrigues
University of Porto (PT)

Conference room

Amphitheater II

Session time

Saturday, June 24, 2017 - 11:30 to 12:30

GT9: Games Robotics and smart technologies

Efthyvoulos Kyriacou

Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)

Conference room

Amphitheater I

Session time

Saturday, June 24, 2017 - 14:45 to 15:45

19 Presentations

On supporting Parkinson's Disease patients: The i-PROGNOSIS Personalized Game Suite design approach

Abstract:

The use of serious games in health care interventions sector has grown rapidly in the last years, however, there is still a gap in the understanding on how these types of interventions are used for the management of the Parkinson Disease (PD), in particular. Targeting intelligent early detection and intervention in PD area, the Personalized Game Suite (PGS) design process approach is presented as part of the H2020 i-PROGNOSIS project that introduces the integration of different serious games in a unified platform (i.e., ExerGames, DietaryGames, EmoGames, and Handwriting/Voice Games).

S. B. Dias

Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)

J. A. Diniz

Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

V Charisis

S Hadjidimitriou

M. Stadtschnitzer

P. Fagerberg

Ioannis Ioakimidis

K Dimitropoulos

N Grammalidis

Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)

Carotid Bifurcation Plaque Stability Estimation based on Motion Analysis

Abstract:

"Through this study we are presenting the initial steps towards a real time motion analysis system to predict the stability of carotid bifurcation plaques. The analysis is performed on B-mode video loops. Loops are analyzed in order to follow systoly and diastoly sections of the cardiac cycle and trace the motion of plaques during these periods. We had created a system that applies Farneback’s optical flow estimation method in order to estimate the flow between consecutive frames or frames at a predefined interval.

Efthyvoulos Kyriacou

Andrew Nicolaides

Alexandra Constantinou

Maura Griffin

Christos P Loizou
Cyprus University of Technology (CY)

Marios S. Pattichis

Hamed Nasrabadi

Constantinos Pattichis
University of Cyprus (CY)

A Versatile Architecture for Building IoT Quantified-Self Applications

Abstract:

The abundance of activity trackers and biosignal sensors as well as the evolution of IoT and communication technologies have considerably advanced the concept of Quantified-Self. Nowadays there are several frameworks and applications that realize the concept, focusing though strictly on specific areas, from daily use to professional activities such as sport and healthcare. This work proposes a versatile, cross-domain solution for building quantified-self applications exploiting the capacities for open-design, modularity and extensibility of the AGILE IoT gateway.

Andreas Menychtas

Charalampos Doukas

Panayiotis Tsanakas

Ilias Maglogiannis
University of Piraeus (GR)

Commercial BCI Control And Functional Brain Networks in Spinal Cord Injury: A Proof-of-Concept.

Abstract:

Spinal Cord Injury (SCI), along with disability, results in changes of brain organization and structure. While sensorimotor networks of patients and healthy individuals share similar patterns, unique functional interactions have been identified in SCI networks. Brain-Computer Interfaces (BCIs) have emerged as a promising technology for movement restoration and rehabilitation of SCI patients. We describe an experimental methodology to combine high-resolution electroencephalography (EEG) for investigation of functional connectivity following SCI and non-invasive BCI control of robotic arms.

Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)

George Arfaras

Ioannis Xygonakis

Panagiotis Kartsidis
Aristotle University of Thessaloniki (GR)

Niki Pandria
Aristotle University of Thessaloniki (GR)

Kyriaki Rafailia Kavazidi
Lab of Medical Physics The Medical School Aristotle University of Thessaloniki

Alexander Astaras
American College of Thessaloniki (GR)

Nicolas Foroglou

Konstantinos Polyzoidis

Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

GT10 Medical Imaging II

Alexandros Tzallas
Technological Educational Institute of Epirus (GR)

Ilias Maglogiannis
University of Piraeus (GR)

Conference room

Amphitheater II

Session time

Saturday, June 24, 2017 - 14:45 to 15:45

19 Presentations

A comparative study of cell nuclei attributed relational graphs for knowledge description and categorization in histopathological gastric cancer whole slide images

Abstract:

In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations, followed by hierarchical ensemble learning and classification.

Harshita Sharma
Technical University Berlin (DE)

Norman Zerbe

Christine BOger

Stephan Wienert

Olaf Hellwich

Peter Hufnagl

Non-Invasive Assessment of Coronary Stenoses and Comparison to Invasive Techniques: a proof-of-concept study

Abstract:

Coronary Computed Tomography Angiography (CCTA) has gained substantial ground in everyday clinical practice due to its non-invasive nature. In this work we present a noninvasive method to assess the hemodynamic significance of coronary stenoses using only CCTA images. Two female patients were subjected to Invasive Coronary Angiography, Virtual Histology IVUS and CCTA. The same arterial segment was reconstructed in 3D using the proposed method as well as two already validated 3D reconstruction methods using the aforementioned invasive techniques.

Panagiota Tsompou

Panagiotis Siogkas
University of Ioannina (GR)

Antonis Sakellarios

Pedro Lemos

Lampros Michalis

Dimitris Fotiadis
University of Ioannina (GR)

Automated collagen proportional area extraction in liver biopsy images using a novel classification via clustering algorithm

Abstract:

Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity. In current work a novel modification of K-means clustering is proposed for image segmentation of liver biopsies. More specifically, supervised restriction of centroids movement is utilized.

Dimosthenis C. Tsouros

Panagiotis N. Smyrlis

Nikolaos Giannakeas

Alexandros Tzallas
Technological Educational Institute of Epirus (GR)

Pinelopi Manousou

Dimitrios G. Tsalikakis

Markos G. Tsipouras
University of Western Macedonia

A Deconstructed Replication of Time of Test Using the AGIS Metric (Skyline paper)

Abstract:

In medical practice, glaucoma severity is usually measured using the Advanced Glaucoma Intervention Studies (AGIS) metric. In a previous study, we replicated the work of Montolio et al.,and demonstrated that, for a larger dataset, time of day of test using the AGIS metric did make a difference to the measurement of glaucoma, supporting Montolio et al’s work. However, in our earlier study, we used the AGIS scores for both eyes combined. In this paper, we use the measurement from just one eye at a time.

Steve Counsell

Stephen Swift
Brunel University (UK)

Allan Tucker
Brunel University London (UK)