C-BER - Indexed Articles in Conferences

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    2D computational modeling of optical trapping effects on malaria-infected red blood cells
    ( 2017) Joana Isabel Paiva ; Ribeiro,RSR ; Pedro Jorge ; Carla Carmelo Rosa ; Guerreiro,A ; João Paulo Cunha
    A computational method for optical fiber trapping of healthy and Malariainfected blood cells characterization is proposed. A trapping force relation with the infection stage was found, which could trigger the development of a diagnostic sensor. © OSA 2017.
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    3D lung nodule candidate detection in multiple scales
    ( 2015) Novo,J ; Goncalves,L ; Ana Maria Mendonça ; Aurélio Campilho
    Lung cancer is mainly diagnosed by the identification of malignant nodules in the lung parenchyma. For that purpose, the identification of all the possible structures that could be suspicious of lung nodules became a crucial task in any lung cancer computer aided diagnosis (CAD) system. In this paper, a new approach for lung nodule candidate identification is proposed. This method uses a 3D medialness Hessian-based filtering to identify round shape structures that could be identified as nodules. This technique, that demonstrated its accuracy in lung vesselness extraction, provides clearer candidates than other approaches, providing less response in the presence of noise artifacts and returns a better continuity in vessels, mostly responsible for false positives. That way, they will be better distinguishable from the nodules in posterior analysis. This approach was validated in 120 scans from the LIDC/IDRI image database. They include 212 nodules with diameters in the range 3 mm to 30 mm. The results demonstrate that our approach is capable of identifying most of the nodules and include less false positives than other approaches, facilitating a posterior task for false positive removal.
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    A 3D multimodal approach to precisely locate DBS electrodes in the basal ganglia brain region
    ( 2015) Nádia Moreira Silva ; Rozanski,VE ; João Paulo Cunha
    Deep Brain Stimulation (DBS) is the effective surgical treatment for drug-refractory movement disorders. In order to improve the therapeutic outcome precise anatomic location of electrodes must be achieved. Thus, neurologists can achieve better clinical decisions and take a more careful selection of the best stimulation parameters for DBS. In this paper, we present a system that accurately obtains the 3D positions of DBS electrodes relative to anatomical structures. The latter is based on the segmentation of deep brain structures and on a multimodal imaging approach. In this study, we examined 16 patients undergoing DBS (8 with Parkinson's disease and 8 with dystonia). A 'neuroscientist friendly' graphic user interface (GUI) was designed to support the processing pipeline to precisely detect the electrodes from the DBS lead. Using this system, we obtained the electrodes position and compared them with the ones manually calculated by an experienced physician. The differences observed were less than a voxel size for 89.9% of the cases and the automated procedure takes less 97.5% time than the manual procedure (1min vs 40min). The resulting masks were congruent in shape and position with the corresponding areas in the individuals' space. Using our automatic segmentation pipeline, clinicians save 77% of their time when compared with a manual segmentation (1.20min vs 5.26min). Both structures and electrodes masks were warped to the MNI space in order to provide a common reference space, for the clinical interpretations. © 2015 IEEE.
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    3D Multimodal Visualization of Subdural Electrodes with Cerebellum Removal to Guide Epilepsy Resective Surgery Procedures
    ( 2014) Nádia Moreira Silva ; Rego,R ; João Paulo Cunha
    Patients with medically refractory epilepsy may benefit from surgical resection of the epileptic focus. Subdural electrodes are implanted to accurately locate the seizure onset and locate the eloquent areas to be spared. However, the visualization of the subdural electrodes may be limited by the current methods. The aim of this work was to assist physicians in the localization of subdural electrodes in relation to anatomical landmarks using co-registration methods and by removing the cerebellum from MRI images. Three patients with refractory epilepsy were studied, in whom subdural electrodes were implanted. All electrodes were correctly localized in a 3D view over the cortex and their visualization was improved by the removal of cerebellum. This method promises to be useful in the optimization of the surgical plan.
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    ABrIL - Advanced Brain Imaging Lab.: a cloud based computation environment for cooperative neuroimaging projects
    ( 2014) Neves Tafula,SMN ; Nádia Moreira Silva ; Rozanski,VE ; João Paulo Cunha
    Neuroscience is an increasingly multidisciplinary and highly cooperative field where neuroimaging plays an important role. Neuroimaging rapid evolution is demanding for a growing number of computing resources and skills that need to be put in place at every lab. Typically each group tries to setup their own servers and workstations to support their neuroimaging needs, having to learn from Operating System management to specific neuroscience software tools details before any results can be obtained from each setup. This setup and learning process is replicated in every lab, even if a strong collaboration among several groups is going on. In this paper we present a new cloud service model - Brain Imaging Application as a Service (BiAaaS) - and one of its implementation - Advanced Brain Imaging Lab (ABrIL) - in the form of an ubiquitous virtual desktop remote infrastructure that offers a set of neuroimaging computational services in an interactive neuroscientist-friendly graphical user interface (GUI). This remote desktop has been used for several multi-institution cooperative projects with different neuroscience objectives that already achieved important results, such as the contribution to a high impact paper published in the January issue of the Neuroimage journal. The ABrIL system has shown its applicability in several neuroscience projects with a relatively low-cost, promoting truly collaborative actions and speeding up project results and their clinical applicability.
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    Acceleration of Tear Film Map Definition on Multicore Systems
    ( 2016) Domínguez,JG ; Beatriz Remeseiro López ; Martín,MJ
    Dry eye syndrome is a public health problem, and one of the most common conditions seen by eye care specialists. Among the clinical tests for its diagnosis, the evaluation of the interference patterns observed in the tear film lipid layer is often employed. In this sense, tear film maps illustrate the spatial distribution of the patterns over the whole tear film and provide useful information to practitioners. However, the creation of a single map usually takes tens of minutes. Medical experts currently demand applications with lower response time in order to provide a faster diagnosis for their patients. In this work, we explore different parallel approaches to accelerate the definition of the tear film map by exploiting the power of today's ubiquitous multicore systems. They can be executed on any multicore system without special software or hardware requirements. The experimental evaluation determines the best approach (on-demand with dynamic seed distribution) and proves that it can significantly decrease the runtime. For instance, the average runtime of our experiments with 50 real-world images on a system with AMD Opteron processors is reduced from more than 20 minutes to one minute and 12 seconds. © The Authors. Published by Elsevier B.V.
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    An adaptive model approach for quantitative wrist rigidity evaluation during deep brain stimulation surgery
    ( 2016) Assis,S ; Costa,P ; Rosas,MJ ; Vaz,R ; João Paulo Cunha
    Intraoperative evaluation of the efficacy of Deep Brain Stimulation includes evaluation of the effect on rigidity. A subjective semi-quantitative scale is used, dependent on the examiner perception and experience. A system was proposed previously, aiming to tackle this subjectivity, using quantitative data and providing real-time feedback of the computed rigidity reduction, hence supporting the physician decision. This system comprised of a gyroscope-based motion sensor in a textile band, placed in the patients hand, which communicated its measurements to a laptop. The latter computed a signal descriptor from the angular velocity of the hand during wrist flexion in DBS surgery. The first approach relied on using a general rigidity reduction model, regardless of the initial severity of the symptom. Thus, to enhance the performance of the previously presented system, we aimed to develop models for high and low baseline rigidity, according to the examiner assessment before any stimulation. This would allow a more patient-oriented approach. Additionally, usability was improved by having in situ processing in a smartphone, instead of a computer. Such system has shown to be reliable, presenting an accuracy of 82.0% and a mean error of 3.4%. Relatively to previous results, the performance was similar, further supporting the importance of considering the cogwheel rigidity to better infer about the reduction in rigidity. Overall, we present a simple, wearable, mobile system, suitable for intra-operatory conditions during DBS, supporting a physician in decision-making when setting stimulation parameters. © 2016 IEEE.
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    Adversarial Synthesis of Retinal Images from Vessel Trees
    ( 2017) Costa,Pedro ; Adrian Galdran ; Maria Inês Meyer ; Ana Maria Mendonça ; Aurélio Campilho
    Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. Here we propose a method that learns to synthesize eye fundus images directly from data. For that, we pair true eye fundus images with their respective vessel trees, by means of a vessel segmentation technique. These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image. For this purpose, we use a recent image-to-image translation technique, based on the idea of adversarial learning. Experimental results show that the original and the generated images are visually different in terms of their global appearance, in spite of sharing the same vessel tree. Additionally, a quantitative quality analysis of the synthetic retinal images confirms that the produced images retain a high proportion of the true image set quality. © Springer International Publishing AG 2017.
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    Assessment of Retinal Vascular Changes Through Arteriolar-to-Venular Ratio Calculation
    ( 2015) Dashtbozorg,B ; Ana Maria Mendonça ; Aurélio Campilho
    The Arteriolar-to-Venular Ratio (AVR) is an index used for the early diagnosis of diseases such as diabetes, hypertension or cardiovascular pathologies. This paper presents three automatic approaches for the estimation of the AVR in retinal images that result from the combination of different methodologies in some of the processing phases used for AVR estimation. Each one of these methods includes vessel segmentation, vessel caliber estimation, optic disc detection or segmentation, region of interest determination, vessel classification into arteries and veins and finally AVR calculation. The values produced by the proposed methods on 40 images of the INSPIRE-AVR dataset were compared with a ground-truth obtained by two medical experts using a semi-automated system. The results showed that the measured AVRs are not statistically different from the reference, with mean errors similar to those achieved by the two experts, thus demonstrating the reliability of the herein proposed approach for AVR estimation.
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    Assessment of Vascular Changes in Retinal Images
    ( 2014) Dashtbozorg,B ; Ana Maria Mendonça ; Aurélio Campilho
    The Arteriolar-to-Venular Ratio (AVR) is a well known index for the early diagnosis of diseases such as diabetes, hypertension or cardio-vascular pathologies. This paper presents an automatic approach for the estimation of the AVR in retinal images. The proposed method includes vessel segmentation, vessel caliber estimation, optic disc detection, region of interest determination, artery/vein classification and finally AVR calculation. This method was evaluated using the images of the INSPIRE-AVR dataset. The mean error of the measured AVR values with respect to the reference ones was 0.05, which is identical to the one achieved by a medical expert using a semi-automated system, thus demonstrating the reliability of the herein proposed solution for AVR estimation.
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    Attention-driven Spatial Transformer Network for Abnormality Detection in Chest X-Ray Images
    ( 2022) Joana Maria Rocha ; Sofia Cardoso Pereira ; João Manuel Pedrosa ; Aurélio Campilho ; Ana Maria Mendonça ; 6071 ; 6381 ; 7623 ; 7800 ; 8251
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    Automated volumetry for unilateral hippocampal sclerosis detection in patients with temporal lobe epilepsy
    ( 2016) Martins,C ; Nádia Moreira Silva ; Silva,G ; Rozanski,VE ; João Paulo Cunha
    Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy (TLE) and can be identified in magnetic resonance imaging as hippocampal atrophy and subsequent volume loss. Detecting this kind of abnormalities through simple radiological assessment could be difficult, even for experienced radiologists. For that reason, hippocampal volumetry is generally used to support this kind of diagnosis. Manual volumetry is the traditional approach but it is time consuming and requires the physician to be familiar with neuroimaging software tools. In this paper, we propose an automated method, written as a script that uses FSL-FIRST, to perform hippocampal segmentation and compute an index to quantify hippocampi asymmetry (HAI). We compared the automated detection of HS (left or right) based on the HAI with the agreement of two experts in a group of 19 patients and 15 controls, achieving 84.2% sensitivity, 86.7% specificity and a Cohen's kappa coefficient of 0.704. The proposed method is integrated in the 'Advanced Brain Imaging Lab' (ABrIL) cloud neurocomputing platform. The automated procedure is 77% (on average) faster to compute vs. the manual volumetry segmentation performed by an experienced physician. © 2016 IEEE.
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    Automatic and semi-automatic approaches for arteriolar-to-venular computation in retinal photographs
    ( 2017) Ana Maria Mendonça ; Beatriz Remeseiro López ; Dashtbozorg,B ; Aurélio Campilho
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    Automatic Characterization of the serous Retinal Detachment Associated with the subretinal Fluid Presence in Optical Coherence Tomography Images
    ( 2018) Novo,J ; Silva,J ; Moura,JD ; Penas,S ; Ortega,M ; Ana Maria Mendonça ; 6381
    An accurate detection of the macular edema (ME) presence constitutes a crucial ophthalmological issue as it provides useful information for the identification, diagnosis and treatment of different relevant ocular and Systemic diseaseS. serous Retinal Detachment (sRD) is a particular type of ME, which is characterized by the leakage of fluid that has a propensity of being accumulated in the macular region. This paper proposes a new methodology for the automatic identification and characterization of the sRD edema using Optical Coherence Tomography (OCT) imageS. The subretinal fluids and the External Limiting Membrane (ELM) retinal layers are identified and characterized to measure the disease severity. Four different visualization modules were designed including representative derived parameters to facilitate the doctor's work in the diagnostic evaluation of ME. The different steps of this method were validated using the manual labelling provided by an expert clinician. The validation of the proposed method offered satisfactory results, constituting a suitable scenario with intuitive visual representations that also include different relevant biomarkerS. © 2018 The Author(s).
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    Automatic Eye Blink Detection Using Consumer Web Cameras
    ( 2015) Beatriz Remeseiro López ; Fernandez,A ; Lira,M
    This research aims to advance blinking detection in the context of work activity. Rather than patients having to attend a clinic, blinking videos can be acquired in a work environment, and further automatically analyzed. Therefore, this paper presents a methodology to perform the automatic detection of eye blink using consumer videos acquired with low-cost web cameras. This methodology includes the detection of the face and eyes of the recorded person, and then it analyzes the low-level features of the eye region to create a quantitative vector. Finally, this vector is classified into one of the two categories considered - open and closed eyes-by using machine learning algorithms. The effectiveness of the proposed methodology was demonstrated since it provides unbiased results with classification errors under 5%.
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    Beat-to-beat ECG features for time resolution improvements in stress detection
    ( 2017) Axman,D ; Joana Isabel Paiva ; de La Torre,F ; João Paulo Cunha
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    Biological imaging with high dynamic range using compressive imaging technique
    ( 2012) Gelareh Babaie ; Faramarz Farahi ; Mehrdad Abolbashari ; Filipe Tiago Magalhães ; Miguel Velhote Correia ; Francisco Araújo ; Awad Gerges
    Scenes in real world have dynamic range of radiation that cannot be captured by conventional cameras. High dynamic range imaging is a technique to capture detail images where, in the field of image, intensity variation is extreme. This technique is very useful for biological imaging where the samples have very bright and very dark regions and both parts have useful information. In this article we propose a novel high dynamic range imaging technique based on compressive imaging that uses one single detector instead of camera (array of detectors) to capture an image. Combination of high dynamic range imaging and compressive imaging benefits from imaging with high dynamic range of radiation and advantages of compressive sampling; namely, imaging at regions of optical spectrum where conventional cameras are not readily available and single detectors are available. Additionally, as its name suggests, this technique requires less number of samples (compared to raster scanning). Our experimen
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    Building up a verified page on facebook using information transparency guidelines
    ( 2017) Alexandre Hostand Souza ; Cappelli,C ; Maciel,C
    Online credibility is a quality pursued by users, business and brands on Internet. Having a verified page on Facebook means improvement of the social web presence, reliability and reinforcement of security against impersonations of identity, unwanted fake pages and spams. Since the Facebook’s page verification request has become more complex and the requirements to receive a verified page badge are uncertain, this paper describes the use of foundations of transparency on information systems to fulfill the data on the forms of the application for verification to improve the success in receiving the verified page status. © Springer International Publishing AG 2017.
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    Central Medialness Adaptive Strategy for 3D Lung Nodule Segmentation in Thoracic CT Images
    ( 2016) Goncalves,L ; Novo,J ; Aurélio Campilho
    In this paper, a Hessian-based strategy, based on the central medialness adaptive principle, was adapted and proposed in a multiscale approach for the 3D segmentation of pulmonary nodules in chest CT scans. This proposal is compared with another well stated Hessian based strategy of the literature, for nodule extraction, in order to demonstrate its accuracy. Several scans from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were employed in the test and validation procedure. The scans include a large and heterogeneous set of 569 solid and mostly solid nodules with a large variability in the nodule characteristics and image conditions. The results demonstrated that the proposal offers correct results, similar to the performance of the radiologists, providing accurate nodule segmentations that perform the desirable scenario for a posterior analysis and the eventual lung cancer diagnosis.
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    Changes in ST, QT and RR ECG intervals during acute stress in firefighters: a pilot study
    ( 2016) Joana Isabel Paiva ; Susana Cristina Rodrigues ; João Paulo Cunha ; 5864 ; 6260 ; 6322
    Firefighting is a stressful occupation. The monitoring of psychophysiological measures in those professionals can be a way to prevent and early detect cardiac diseases and other stress-related problems. The current study aimed to assess morphological changes in the ECG signal induced by acute stress. A laboratory protocol was conducted among 6 firefighters, including a laboratory stress-inducer task - the Trier Social Stress Task (TSST) - and a 2-choice reaction time task (CRTT) that was performed before (CRTT1) and after (CRTT2) the stress condition. ECG signals were continuously acquired using the VitalJacket (R), a wearable t-shirt that acts as a medical certified ECG monitor. Results showed that ECG morphological features such as QT and ST intervals are able to differentiate stressful from non stressful events in first responders. Group mean Visual Analogue Scale (VAS) for stress assessment significantly increased after the stress task (TSST), relatively to the end of CRTT2 (after TSST: 4.67 +/- 1.63; after CRTT2: 3.17 +/- 0.75), a change that was accompanied by a significant increase in group mean QT and ST segments corrected for heart rate during TSST. These encouraging results will be followed by larger studies in order to explore those measures and its physiological impact under realistic environments in a higher scalability.