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• We propose a novel Deep Learning model, namely the ResQu-Net, specifically tailored for Peripheral Zone Segmentation on T2W MR images. • Six DL architectures were implemented and compared using three openly available datasets with 392 patient cases. • Qualitative Explainability Analysis reveals that conventional segmentation metrics, such as Dice Score, and Hausdorff Distance, might not reflect properly the performance of a DL segmentation model. Prostate cancer is a leading cause of male cancer worldwide. With more than 70 % of prostate cancers arising in the peripheral zone of the prostate, accurate segmentation of this region is of paramount importance for the effective diagnosis and treatment of the disease. Although peripheral zone is well recognized as one of the most challenging regions to delineate within the prostate, no algorithms specifically tailored for this segmentation task are currently available. The present study introduces a new deep learning (DL) algorithm, named as ResQu-Net, which is designed to accurately segment the peripheral zone (PZ) of the prostate on T2-weighted magnetic resonance imaging (MRI). Using three publicly available datasets, the ResQu-Net outperformed the six DL segmentation models used for comparison, namely the Attention U-Net, the Dense2U-Net, the Proper-Net, the TransU-net, the U-Net, and the USE-Net, demonstrating superior performance for different anatomical regions, such as the apex, the midgland and the base. The assessment of the suggested approach was conducted not only quantitatively (Sensitivity, Balanced Accuracy, Dice Score, 95 % Hausdorff Distance, and Average Surface Distance) but also qualitatively. For the qualitative evaluation the feature maps obtained from the last layers of each model were compared with the Density Map of the Ground Truth annotations using root mean squared error. Overall, the ResQu-Net model exhibits improved performance compared to other models, of more than 5 % and 1.87 mm in terms of Dice Score and 95 % Hausdorff Distance, respectively. These advancements may contribute significantly in addressing the challenges associated with PZ segmentation, and ultimately enabling improved clinical decision-making and patient outcomes. [ABSTRACT FROM AUTHOR]

• T2, T2* relaxometry can be used to support diagnosis for lipomatous neoplasms. • The amount of signal loss related to spin coupling phenomena is indicative of lipomatous tumor histological grading. • Pre-operative biopsy guidance can be facilitated as areas of good or poor differentiation are identified on a pixel basis. Subcutaneous fat may have variable signal intensity on T2w images depending on the choice of imaging parameters. However, fatty components within tumors have a different degree of signal dependence on the acquisition scheme. This study examined the use of T2, T2* relaxometry and spin coupling related signal changes (Spin Coupling ratio, SCr) on two different imaging protocols as clinically relevant descriptors of benign and malignant lipomatous tumors. 20 patients with benign lipomas or liposarcomas of variable histologic grade were examined at an 1.5 T scanner with Multi Echo Spin Echo (MESE) different echo spacing (ESP) in order to produce bright fat T2w images (ESP: 13.4 ms, 25 equidistant echoes) and dark fat images (ESP: 26.8 ms with 10 equidistant echoes). T2* relaxometry acquisition comprises 4 sets of in-opposed echoes (2.4–19.2 ms, ESP: 2.4 ms) Multi Echo Gradient Echo (MEGRE) sequence. All parametric maps were calculated on a pixel basis. Significant differences of SCr were found for five different types of lipomatous tumors (Pairwise t -test with Bonferroni correction): lipomas, well differentiated liposarcomas, myxoid liposarcomas, pleomorphic liposarcomas and poorly differentiated liposarcomas. SCr surpassed the classification performance of T2 and T2* relaxometry. A novel biomarker based on spin coupling related signal loss, SCr, is indicative of lipomatous tumor histological grading. We concluded that T2, T2* and SCr can be used for the classification of fat containing tumors, which may be important for biopsy guidance in heterogeneous masses and treatment planning. [ABSTRACT FROM AUTHOR]

To assess the progression of coronary artery disease, Optical Coherence Tomography (OCT) pullbacks acquired at different timepoints should be compared. However, the assessment of temporal sequences is a difficult task, as motion artifacts in the longitudinal and axial plane can decrease the quality of the manual inspection. To address this challenge, the current study presents a two-stage computational framework for the longitudinal and axial registration of two OCT pullbacks. During the first stage of the process, we focus on the accurate detection of the matching image pairs from the respective series, while during the second stage we focus on the axial registration of the matched pairs so that their common features are aligned. The dataset used includes 19 patients from two clinical centers, with two OCT pullbacks per patient: one before the stent implantation procedure and one after it. We applied our method on a synthetic dataset of OCT pullbacks, which was generated based on the in-vivo OCT pullbacks to reproduce the motion artifacts across the planes. In addition, the proposed method was validated on the 19 pairs of in-vivo OCT pullbacks with annotated pre/post stent deployment data. The method was able to reduce the alignment error from 32. 17 ± 26. 14 to 5. 6 ± 6. 6 frames, the rotational error from 11. 59 ° ± 11. 22 ° to 1. 18 ° ± 0. 81 ° and the distance error from 3. 07 m m ± 1. 52 m m to 0. 46 m m ± 0. 44 m m. In addition, the mean Mutual Information similarity increased by 13.47% after the longitudinal registration and an additional 123.33% after the axial registration on top of the previous one. • Longitudinal and axial registration of OCT pullbacks. • DTW algorithm based on lumen contour's features for OCT longitudinal registration. • Modified Mutual Information based on a variant of Harmony Search optimizer. • Validation of the method 19 patients achieved state of the art performance. [ABSTRACT FROM AUTHOR]

The objective of the INTEGRATE project (http://www.fp7-integrate.eu/) that has recently concluded successfully was the development of innovative biomedical applications focused on streamlining the execution of clinical research, on enabling multidisciplinary collaboration, on management and large-scale sharing of multi-level heterogeneous datasets, and on the development of new methodologies and of predictive multi-scale models in cancer. In this paper, we present the way the INTEGRATE consortium has approached important challenges such as the integration of multi-scale biomedical data in the context of post-genomic clinical trials, the development of predictive models and the implementation of tools to facilitate the efficient execution of postgenomic multi-centric clinical trials in breast cancer. Furthermore, we provide a number of key "lessons learned" during the process and give directions for further future research and development. [ABSTRACT FROM AUTHOR]

Background: New community-based arrangements and novel technologies can empower individuals to be active participants in their health maintenance, enabling people to control and self-regulate their health and wellness and make better health- and lifestyle-related decisions. Mobile sensing technology and health systems responsive to individual profiles combined with cloud computing can expand innovation for new types of interoperable services that are consumer-oriented and community-based. This could fuel a paradigm shift in the way health care can be, or should be, provided and received, while lessening the burden on exhausted health and social care systems.Objective: Our goal is to identify and discuss the main scientific and engineering challenges that need to be successfully addressed in delivering state-of-the-art, ubiquitous eHealth and mHealth services, including citizen-centered wellness management services, and reposition their role and potential within a broader context of diverse sociotechnical drivers, agents, and stakeholders.Methods: We review the state-of-the-art relevant to the development and implementation of eHealth and mHealth services in critical domains. We identify and discuss scientific, engineering, and implementation-related challenges that need to be overcome to move research, development, and the market forward.Results: Several important advances have been identified in the fields of systems for personalized health monitoring, such as smartphone platforms and intelligent ubiquitous services. Sensors embedded in smartphones and clothes are making the unobtrusive recognition of physical activity, behavior, and lifestyle possible, and thus the deployment of platforms for health assistance and citizen empowerment. Similarly, significant advances are observed in the domain of infrastructure supporting services. Still, many technical problems remain to be solved, combined with no less challenging issues related to security, privacy, trust, and organizational dynamics.Conclusions: Delivering innovative ubiquitous eHealth and mHealth services, including citizen-centered wellness and lifestyle management services, goes well beyond the development of technical solutions. For the large-scale information and communication technology-supported adoption of healthier lifestyles to take place, crucial innovations are needed in the process of making and deploying usable empowering end-user services that are trusted and user-acceptable. Such innovations require multidomain, multilevel, transdisciplinary work, grounded in theory but driven by citizens' and health care professionals' needs, expectations, and capabilities and matched by business ability to bring innovation to the market. [ABSTRACT FROM AUTHOR]

In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system, utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets. [ABSTRACT FROM AUTHOR]

As the field of brain monitoring is evolving rapidly, there is an increasing demand for innovative approaches to handle relevant signals. Recently, graph signal processing, which enables the treatment of signal ensembles, emerges as a powerful alternative to a per-signal analysis. This is especially the case for electroencephalogram (EEG) signals that naturally admit graph representations, with each electrode corresponding to one graph node. These signals are often corrupted by impulsive noise best characterized by heavy-tailed statistics, thus driving conventional denoising techniques to failure. To address this problem, we propose an efficient regularized graph filtering method based on fractional lower-order moments, which better adapt to heavy-tailed statistics. An experimental evaluation on real EEG measurements, including the publicly available P300 dataset and epilepsy signals, reveals a superior denoising performance of our method when compared against well-established EEG signal denoising methods. • Graph-based denoising of impulsive signals. • Symmetric alpha-stable theory applied on EEG signals. • Graph representation of EEG signals in impulsive environments. [ABSTRACT FROM AUTHOR]

• Investigation of the cross recurrence quantification analysis (CRQA). • Application on 16 brain regions which consist the visuomotor network. • Comparison with static functional connectivity (FC)-Pearson's correlation. • Comparison with temporal-based dFC alternative approach. • CRQA proved to be more efficient in separating NPSLE patients from HC volunteers. There is growing interest in dynamic approaches to functional brain connectivity (FC), and their potential applications in understanding atypical brain function. In this study, we assess the relative sensitivity of cross recurrence quantification analysis CRQA) to identify aberrant FC in patients with neuropsychiatric systemic lupus erythematosus (NPSLE) in comparison with conventional static and dynamic bivariate FC measures, as well as univariate (nodal) RQA. This technique was applied to resting-state fMRI data obtained from 45 NPSLE patients and 35 healthy volunteers (HC). Cross recurrence plots were computed for all pairs of 16 frontoparietal brain regions known to be critically involved in visuomotor control and suspected to show hemodynamic disturbance in NPSLE. Multivariate group comparisons revealed that the combination of six CRQA measures differentiated the two groups with large effect sizes (. 214 > η 2 >. 061) in 40 out of the 120 region pairs. The majority of brain regions forming these pairs also showed group differences on nodal RQA indices (. 146 > η 2 >. 09) Overall, larger values were found in NPSLE patients vs. HC with the exception of FC formed by the paracentral lobule. Determinism within five pairs of right-hemisphere sensorimotor regions (paracentral lobule, primary somatosensory, primary motor, and supplementary motor areas), correlated positively with visuomotor performance among NPSLE patients (p <. 001). By comparison, group differences on static FC displayed large effect sizes in only 4 of the 120 region pairs (. 126 > η 2 >. 061), none of which correlated significantly with visuomotor performance. Indices derived from dynamic, temporal-based FC analyses displayed large effect sizes in 11 / 120 region pairs (. 11 > η 2 >. 063). These findings further support the importance of feature-based dFC in advancing current knowledge on correlates of cognitive dysfunction in a clinically challenging disorder, such as NPSLE. [ABSTRACT FROM AUTHOR]

Abstract: Personal health record (PHR) systems are a constantly evolving area in the field of health information technology which motivates an ongoing research towards their evaluation in several different aspects. In this direction, we present an evaluation study on PHR systems that provides an insight on their current status with regard to functional and technical capabilities and we present our extensions to a specific PHR system. Essentially, we provide a requirement analysis that formulates our composite evaluation model which we use to perform a systems review on numerous available solutions. Then, we present our development efforts towards an intelligent PHR system. [Copyright &y& Elsevier]

Background: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed.Objective: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects.Methods: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed.Results: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work.Conclusions: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations. [ABSTRACT FROM AUTHOR]

As a result of recent advances in cancer research and "precision medicine" approaches, i.e. the idea of treating each patient with the right drug at the right time, more and more cancer patients are being cured, or might have to cope with a life with cancer. For many people, cancer survival today means living with a complex and chronic condition. Surviving and living with or beyond cancer requires the long-term management of the disease, leading to a significant need for active rehabilitation of the patients. In this paper, we present a novel methodology employed in the iManageCancer project for cancer patient empowerment in which personal health systems, serious games, psychoemotional monitoring and other novel decision-support tools are combined into an integrated patient empowerment platform. We present in detail the ICT infrastructure developed and our evaluation with the involvement of cancer patients on two sites, a large-scale pilot for adults and a small-scale test for children. The evaluation showed mixed evidences on the improvement of patient empowerment, while ability to cope with cancer, including improvement in mood and resilience to cancer, increased for the participants of the adults' pilot. [ABSTRACT FROM AUTHOR]

Purpose T2 relaxation constant has been established as an accurate biomarker from the early days of MRI for tissue or material identification as it expresses physical properties without dependence on the MR protocol used. T2 * expresses acceleration of T2 dephasing process by local field inhomogeneities that can be produced by paramagnetic blood products, i.e. deoxyhemoglobin, hemosiderin. Therefore, long R2 ′  = 1/T2 *  − 1/T2 relaxation rates are found in tissues with low oxygenation and/or high oxygen consumption. The present is a study of T2 and R2 ′ for the differentiation between benign and malignant lipomatous tumors. Methods 16 patients with lipomatous tumors underwent preoperatively MRI examination including T2/T2 * relaxometry protocol. T2 relaxometry data were acquired from a Multi Echo Spin Echo CPMG sequence with initial echo at 26.8 ms followed by 9 equidistant echoes (echo spacing 26.8 ms) to avoid spin coupling signal modulation. T2 * protocol comprised 4 opposed-phased echoes (TE: 2.38/7.18/12.0/16.82 ms) and 4 in-phase echoes (4.77/9.59/14.41/19.23 ms). Histologic examination of surgical specimen showed 4 benign lipomas (bl), 4 well differentiated liposarcomas (wdl, Histologic Specific Differentiation Score (HSDS) 1), 2 non round cell myxoid liposarcomas (intermediately differentiated sarcomas, idl, HSDS 2) and 6 poorly differentiated liposarcomas (pdl, HSDS 3). 3D ROI based measurements were performed on areas indicated from histopathologic analysis as indicative of histologic subtype. Results Average T2 (SD) constant for bl/wdl/idl/pdl is 102.2 (1.6)/101.9 (6.7)/562.55 (120.1)/131.8 (12.1) ms. R2 ′ for bl/wdl/idl/pdl is 23.1 (1.1)/17.2 (9.3)/1.6 (0.5)/4.6 (1.0) ms −1 . Similar metrics were taken on healthy subcutaneous tissue to ensure protocol robustness on healthy tissue (Normal fat T2/R2 ′ range: 98.9–102.0 ms/24.2–25.9 ms −1 ). Conclusion Average T2 differs significantly between lipomatous masses of variable differentiation score in a limited sample of benign and malignant lipomatous tumors. R2 ′ is larger for lipomas and well differentiated liposarcomas compared to intermediate or poorly differentiated liposarcomas suggesting lower oxygen supply/consumption of the lesion. [ABSTRACT FROM AUTHOR]

Purpose Non-invasive characterization of lipomatous tumors can be challenging as several histological types have similar imaging characteristics. In this study we examine the use of a new biomarker based on spin coupling related signal loss between two acquisitions of different echo spacing to differentiate between benign lipomas, well, intermediate and poorly differentiated liposarcomas (l, wdl, idl and pdl, respectively). This study was based on previous work showing differences between vegetable oils of different botanical origin using the same protocol [1] . Methods Fourteen patients (9 male, 5 female, age: 37–87, mean 58) with soft tissue masses (5 lipomas, 2 myxoid, 5 dedifferentiated, 2 pleiomorphic liposarcomas) underwent MRI prior to any therapeutic intervention. MRI protocol, among other sequences, included two Multi Echo Spin Echo CPMG sequences with different echo spacing, 13.4 and 26.8 ms respectively, i.e. above and below the approximate threshold of 20 ms in order to have bright and dark fat appearance on T2-w images. All surgically excised specimen were histopathologically examined to determine the kind of lipomatous tumor and to localize sites of well or poor differentiation in the cases of dedifferentiated liposarcomas as distance from the upper tumor limit (z) and distance from the center (x,y). Relative signal loss between bright and dark fat images on TE 80 ms was calculated in order to measure the spin coupling Ratio (Rsc), defined as mean ROI value in the lesion divided by the same value in normal subcutaneous fat for the same patient. Results Mean (SD) of Rsc for l, wdl, idl and pdl was 1.036 (0.06), 0.77 (0.18), 0.055 (0.06) and −0.16 (0.57), respectively. Conclusions A new biomarker related on spin coupling signal loss is indicative of the differentiation grade of lipomatous tumors, with special interest regarding the clinically challenging question of benign lipomas vs. well differentiated liposarcomas. It is of note that Rsc decreases with increased differentiation grade (1–3). [ABSTRACT FROM AUTHOR]

Purpose Radiological evaluation of tumor aggressiveness is very frequently based on diffusion and/or perfusion imaging findings and conclusions are used to guide biopsy. The present work describes a post processing process that highlights areas of increased cellularity (low ADC) and also increased vascular transendothelial permeability (high K trans ), two of the most significant markers of malignancy, at the early stage of imaging. Preliminary results tested on 5 patients with soft tissue sarcomas are presented. Methods Quantitative ADC maps were generated from MR data by pixelwise mono-exponential fitting of multi-b (8b, 0–1500) DW images with custom-built tools written in Python. Similarly, K trans map was calculated based on the Extended Tofts Model from T1-w GRE data (temporal resolution 7 s, 45 time points). All pixel values assigned as tumor volume (3D ROI) were used as input for the initial whole tumor ADC and K trans histograms. As a next step, only pixels with values lower than the mean of ADC histogram and K trans values greater than the mean K trans were located and visualized in order to examine if a voxel cluster with adequate size is discriminated after thresholding. Patient population (5 male, mean age 60) comprised 3 dedifferentiated liposarcomas and 2 pleomorphic liposarcomas. Results Whole tumor pixel percentages with ADC value below the mean ADC value of the same ROI (used as histogram threshold) for the five high grade liposarcomas were: 58.7, 82.5, 52.1, 65.7, and 78.8%. Ktrans pixel percentages above the mean K trans (threshold) were: 11.8, 23.4, 4.3, 62.9, 39.9% respectively. The percentage of pixels meeting both criteria for low ADC and high K trans were: 4.2, 14.2, 0.6, 20.1, 15.3% respectively. Conclusion For 4 out of 5 patients visualization of a pixel cluster with adequate size that can be proposed as suitable site for preoperative needle biopsy was possible. Further appropriate ADC/ K trans thresholding can be used to increase disease conspicuity under certain criteria, depending on the special characteristics of each tumor subtype. [ABSTRACT FROM AUTHOR]