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Objectives: To define the potential, limitations and synergies of micro-CT and other non-radiological techniques for the quantification of emphysema and related processes in mice, by performing a complete characterization of the elastase-induced emphysema model.Materials and Methods: Ninety A/J mice (45 treated and 45 controls) were studied at different time points using breath-hold gated micro-CT, functional test parameters, RT-PCR for RNA cytokine expression, Luminex technology for cytokine plasma concentration and histomorphometry.Results: Both histomorphometry and micro-CT imaging reflect rapid initial emphysema progression followed by steady-state development at decreasing rates. Cytokine measurements reveal an acute inflammatory response within the first 24 h that disappears after the first week. Limited systemic effect was observed based on plasma cytokine concentration. Lung compliance decreases during the acute inflammation phase and increases afterwards.Conclusion: Histomorphometry is the most sensitive technique since it detects airspace enlargement before the other methods (1 h after treatment). Micro-CT correlates well with histology (r2 = 0.63) proving appropriate for longitudinal studies. Functional test parameters do not necessarily correlate with the extent of emphysema, as they can be influenced by acute inflammation. Finally, cytokine measurements correlate with the presence of inflammation in histology but not with emphysema. [ABSTRACT FROM AUTHOR]

Abstract Background Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) predicts adverse prognosis in patients with stable coronary artery disease (CAD). However, the interaction with conventional risk factors remains uncertain. Our aim was to assess whether the extent of LGE is an independent predictor of adverse cardiac outcome beyond conventional risk factors, including left ventricle ejection fraction (LVEF). Methods We enrolled 376 patients (88% males, 64 ± 11 years) with stable CAD, who underwent LGE assessment and a detailed conventional evaluation (clinical and pharmacological history, risk factors, ECG, Echocardiography). During a follow-up of 38 ± 21 months, 56 events occurred (32 deaths, 24 hospitalizations for heart failure). Results LGE and LVEF showed the strongest univariate associations with end-points (HR: 13.61 [95%C.I.: 7.32-25.31] for LGE ≥ 45% of LV mass; and 12.34 [6.80-22.38] for LVEF ≤ 30%; p p p p p Conclusions This study demonstrates that LGE assessed by CMR is a robust independent non-invasive marker of prognosis in stable CAD patients. LGE can integrate the available metrics to substantially improve risk stratification.

An abstract of the study "Temporal Changes of Prognostic Indicators in the Chronic Heart Failure: Frequency and Clinical Meaning," by M. Ceresa and O. Febo, is presented.

An abstract of a study which evaluated the reproducibility of an incremental protocol of 10 watts/minute, frequently applied in heart failure patients as it enables a better heart rate adaptation to physical exercise, is presented.

Objective: This study compared the effectiveness and cost/utility ratio between a heart failure (HF) management program delivered by day-hospital (DH) and usual care in chronic heart failure (CHF) outpatients.Background: Previous studies showed that about 50% of readmissions for CHF can be prevented by a multidisciplinary approach. However, the performance, effectiveness, and cost/utility ratio of a process of HF outpatient management related to evidence-based medicine have not been considered.Methods: A total of 234 prospective patients discharged by a HF Unit were randomized to two management strategies: 122 patients to usual community care and 112 patients to a HF management program delivered by the DH. Management (rate of readmissions, therapeutic interventions), functional parameters (New York Heart Association [NYHA] functional class, left ventricular diameters, and ejection fraction, deceleration time of early diastolic mitral flow, peak oxygen uptake, and mitral regurgitation) and hard outcomes (cardiac death and urgent cardiac transplantation) were evaluated. The cost/utility ratios of the two strategies were compared.Results: After 12 +/- 3 months of follow-up, the individual rate access in DH was 5.5 +/- 3.8 days. The DH subjects were readmitted to the hospital less frequently than were the usual-care group patients (13 vs. 78, p < 0.00001). Patients allocated to usual-care management showed heterogeneous changes in NYHA functional class (13% improved and 16% worsened p = NS); In contrast, the DH group showed significant changes in NYHA functional class (23% improved and 11% worsened, p < 0.009). Hard cardiac events in the one-year follow-up occurred in 25/234 (10.6%) patients; cardiac death occurred in 21/122 (17.2%) of the community group and in 3/112 (2.7%) in the DH group (p < 0.0007). One DH patient underwent urgent transplantation. Comparison of the two managerial models by Cox regression analysis showed that DH management significantly protected against the appearance of hard events (relative risk [RR] 0.17; confidence interval [CI] 0.06 to 0.66). The cost/utility ratio of the two management strategies was similar (usual care $2,409 vs. DH $2,244). The incremental analysis revealed a cost savings of $1,068 for each quality-adjusted life year gained. The cost/utility ratio for the integration of DH management of CHF was $19,462 (CI $13,904 to $34,048).Conclusions: A heart failure outpatient management program delivered by a DH can reduce mortality and morbidity of CHF patients. This management strategy is cost-effective and has an equitable value from a societal point of view. [ABSTRACT FROM AUTHOR]

Objectives: To identify prognostic models which estimate the risk of critical COVID-19 in hospitalized patients and to assess their validation properties., Study Design and Setting: We conducted a systematic review in Medline (up to January 2021) of studies developing or updating a model that estimated the risk of critical COVID-19, defined as death, admission to intensive care unit, and/or use of mechanical ventilation during admission. Models were validated in two datasets with different backgrounds (HM [private Spanish hospital network], n = 1,753, and ICS [public Catalan health system], n = 1,104), by assessing discrimination (area under the curve [AUC]) and calibration (plots)., Results: We validated 18 prognostic models. Discrimination was good in nine of them (AUCs ≥ 80%) and higher in those predicting mortality (AUCs 65%-87%) than those predicting intensive care unit admission or a composite outcome (AUCs 53%-78%). Calibration was poor in all models providing outcome's probabilities and good in four models providing a point-based score. These four models used mortality as outcome and included age, oxygen saturation, and C-reactive protein among their predictors., Conclusion: The validity of models predicting critical COVID-19 by using only routinely collected predictors is variable. Four models showed good discrimination and calibration when externally validated and are recommended for their use., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The aim of this study was to evaluate cardiac rehabilitation (CR)-derived predictors of outcome in patients discharged from rehabilitation after transcatheter aortic valve replacement (TAVR)., Methods: We retrospectively analyzed data from 232 TAVR patients (aged 82±6 years, 55% females) discharged following an average 3-week residential CR program in the period January 2009 to December 2017. Comorbidities (cumulative illness rated state-comorbidity index, CIRS-CI), echocardiography on admission, disability (Barthel Index [BI]) and functional capacity (6-min walk distance, 6MWD) at discharge, and maximal training session intensity expressed in METs/min were collected. The endpoint was all-cause mortality., Results: Seventy-four (32%) deaths occurred at 3-year follow-up. At discharge, non-survivors had a higher comorbidity rate (CIRS-CI 5.2±2.3 vs. 4.1±1.9, P=0.000), higher disability level (BI 80.4±24 vs. 88.8±17, P=0.000), and worse renal function (creatinine 1.6±0.9 vs. 1.2±0.4 mg/dL, P=0.000). They were also more often on diuretics (73% vs. 53.2%, P=0.003) and beta-blocker therapy (73% vs. 57.6%, P=0.042) and had a markedly reduced functional capacity (6MWD 221±100m vs. 265±105m, P=0.001). At multivariate Cox proportional hazards regression analysis, independent predictors of survival at follow-up were lower comorbidity rate, a better-preserved renal function, lower use of diuretics, and a higher 6MWD at discharge (Harrell's C = 0.707)., Conclusions: Patients attending residential CR after TAVR are very old with significant comorbidity. The overall 3-year mortality rate after CR discharge is high. Our findings suggest the need for individually tailored follow-up care in patients discharged from CR after TAVR to address their residual exercise capacity, comorbidities, and renal function impairment.

Advanced visual computing solutions and three-dimensional (3D) printing are moving from engineering to clinical pipelines for training, planning, and guidance of complex interventions. 3D imaging and rendering, virtual reality (VR), and in-silico simulations, as well as 3D printing technologies provide complementary information to better understand the structure and function of the organs, thereby improving and personalizing clinical decisions. In this study, we evaluated several advanced visual computing solutions, such as web-based 3D imaging visualization, VR, and computational fluid simulations, together with 3D printing, for the planning of the left atrial appendage occluder (LAAO) device implantations. Six cardiologists tested different technologies in pre-operative data of five patients to identify the usability, limitations, and requirements for the clinical translation of each technology through a qualitative questionnaire. The obtained results demonstrate the potential impact of advanced visual computing solutions and 3D printing to improve the planning of LAAO interventions as well as the need for their integration into a single workflow to be used in a clinical environment., Competing Interests: The authors declare that they have no competing interests., (Copyright: © 2022 Author(s).)

Predicting whether a lung nodule will grow, remain stable or regress over time, especially early in its follow-up, would help doctors prescribe personalized treatments and better surgical planning. However, the multifactorial nature of lung tumour progression hampers the identification of growth patterns. In this work, we propose a deep hierarchical generative and probabilistic network that, given an initial image of the nodule, predicts whether it will grow, quantifies its future size and provides its expected semantic appearance at a future time. Unlike previous solutions, our approach also estimates the uncertainty in the predictions from the intrinsic noise in medical images and the inter-observer variability in the annotations. The evaluation of this method on an independent test set reported a future tumour growth size mean absolute error of 1.74 mm, a nodule segmentation Dice's coefficient of 78% and a tumour growth accuracy of 84% on predictions made up to 24 months ahead. Due to the lack of similar methods for providing future lung tumour growth predictions, along with their associated uncertainty, we adapted equivalent deterministic and alternative generative networks (i.e., probabilistic U-Net, Bayesian test dropout and Pix2Pix). Our method outperformed all these methods, corroborating the adequacy of our approach.

Recent advances in fetal imaging open the door to enhanced detection of fetal disorders and computer-assisted surgical planning. However, precise segmentation of womb's tissues is challenging due to motion artifacts caused by fetal movements and maternal respiration during acquisition. This work aims to efficiently segment different intrauterine tissues in fetal magnetic resonance imaging (MRI) and 3D ultrasound (US). First, a large set of ninety-four radiomic features are extracted to characterize the mother uterus, placenta, umbilical cord, fetal lungs, and brain. The optimal features for each anatomy are identified using both K-best and Sequential Forward Feature Selection techniques. These features are then fed to a Support Vector Machine with instance balancing to accurately segment the intrauterine anatomies. To the best of our knowledge, this is the first time that "Radiomics" is expanded from classification tasks to segmentation purposes to deal with challenging fetal images. In addition, we evaluate several state-of-the-art deep learning-based segmentation approaches. Validation is extensively performed on a set of 60 axial MRI and 3D US images from pathological and clinical cases. Our results suggest that combining the selected 10 radiomic features per anatomy along with DeepLabV3+ or BiSeNet architectures for MRI, and PSPNet or Tiramisu for 3D US, can lead to the highest fetal / maternal tissue segmentation performance, robustness, informativeness, and heterogeneity. Therefore, this work opens new avenues for advancement of segmentation techniques and, in particular, for improved fetal surgical planning., (Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists. Several lung CT scan images taken at different time points of a given patient need to be individually inspected, looking for possible cancerogenous nodules. Radiologists mainly focus their attention in nodule size, density, and growth to assess the existence of malignancy. In this study, we present a novel method based on a 3D siamese neural network, for the re-identification of nodules in a pair of CT scans of the same patient without the need for image registration. The network was integrated into a two-stage automatic pipeline to detect, match, and predict nodule growth given pairs of CT scans. Results on an independent test set reported a nodule detection sensitivity of 94.7%, an accuracy for temporal nodule matching of 88.8%, and a sensitivity of 92.0% with a precision of 88.4% for nodule growth detection., Competing Interests: Declaration of Competing Interest The authors declare that they do not have any financial or nonfinancial conflict of interests, (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background and Objectives: Electroporation is the phenomenon by which cell membrane permeability to ions and macromolecules is increased when the cell is briefly exposed to high electric fields. In electroporation-based treatments, such exposure is typically performed by delivering high voltage pulses across needle electrodes in tissue. For a given tissue and pulsing protocol, an electric field magnitude threshold exists that must be overreached for treatment efficacy. However, it is hard to preoperatively infer the treatment volume because the electric field distribution intricately depends on the electrodes' positioning and length, the applied voltage, and the electric conductivity of the treated tissues. For illustrating such dependencies, we have created EView (https://eview.upf.edu), a web platform that estimates the electric field distribution for arbitrary needle electrode locations and orientations and overlays it on 3D medical images., Methods: A client-server approach has been implemented to let the user set the electrode configuration easily on the web browser, whereas the simulation is computed on a dedicated server. By means of the finite element method, the electric field is solved in a 3D volume. For the sake of simplicity, only a homogeneous tissue is modeled, assuming the same properties for healthy and pathologic tissues. The non-linear dependence of tissue conductivity on the electric field due to the electroporation effect is modeled. The implemented model has been validated against a state of the art finite element solver, and the server has undergone a heavy load test to ensure reliability and to report execution times., Results: The electric field is rapidly computed for any electrode and tissue configuration, and alternative setups can be easily compared. The platform provides the same results as the state of the art finite element solver (Dice = 98.3 ± 0.4%). During the high load test, the server remained responsive. Simulations are computed in less than 2 min for simple cases consisting of two electrodes and take up to 40 min for complex scenarios consisting of 6 electrodes., Conclusions: With this free platform we provide expert and non-expert electroporation users a way to rapidly model the electric field distribution for arbitrary electrode configurations., Competing Interests: Declaration of Competing Interest Dr. Davalos has patents in the area of electroporation and may receive royalties. He is also a co-founder of a startup company developing electroporation-based therapies for neurological disorders. Natalie Beitel-White has a pending patent in electroporation and may receive royalties., (Copyright © 2020. Published by Elsevier B.V.)

Twin-to-twin transfusion syndrome (TTTS) is characterized by an unbalanced blood transfer through placental abnormal vascular connections. Prenatal ultrasound (US) is the imaging technique to monitor monochorionic pregnancies and diagnose TTTS. Fetoscopic laser photocoagulation is an elective treatment to coagulate placental communications between both twins. To locate the anomalous connections ahead of surgery, preoperative planning is crucial. In this context, we propose a novel multi-task stacked generative adversarial framework to jointly learn synthetic fetal US generation, multi-class segmentation of the placenta, its inner acoustic shadows and peripheral vasculature, and placenta shadowing removal. Specifically, the designed architecture is able to learn anatomical relationships and global US image characteristics. In addition, we also extract for the first time the umbilical cord insertion on the placenta surface from 3D HD-flow US images. The database consisted of 70 US volumes including singleton, mono- and dichorionic twins at 17-37 gestational weeks. Our experiments show that 71.8% of the synthesized US slices were categorized as realistic by clinicians, and that the multi-class segmentation achieved Dice scores of 0.82 ± 0.13, 0.71 ± 0.09, and 0.72 ± 0.09, for placenta, acoustic shadows, and vasculature, respectively. Moreover, fetal surgeons classified 70.2% of our completed placenta shadows as satisfactory texture reconstructions. The umbilical cord was successfully detected on 85.45% of the volumes. The framework developed could be implemented in a TTTS fetal surgery planning software to improve the intrauterine scene understanding and facilitate the location of the optimum fetoscope entry point.

Purpose: Twin-to-twin transfusion syndrome (TTTS) is a serious condition that occurs in about 10-15% of monochorionic twin pregnancies. In most instances, the blood flow is unevenly distributed throughout the placenta anastomoses leading to the death of both fetuses if no surgical procedure is performed. Fetoscopic laser coagulation is the optimal therapy to considerably improve co-twin prognosis by clogging the abnormal anastomoses. Notwithstanding progress in recent years, TTTS surgery is highly risky. Computer-assisted planning of the intervention can thus improve the outcome., Methods: In this work, we implement a GPU-accelerated random walker (RW) algorithm to detect the placenta, both umbilical cord insertions and the placental vasculature from Doppler ultrasound (US). Placenta and background seeds are manually initialized in 10-20 slices (out of 245). Vessels are automatically initialized in the same slices by means of Otsu thresholding. The RW finds the boundaries of the placenta and reconstructs the vasculature., Results: We evaluate our semiautomatic method in 5 monochorionic and 24 singleton pregnancies. Although satisfactory performance is achieved on placenta segmentation (Dice ≥ 84.0%), some vascular connections are still neglected due to the presence of US reverberation artifacts (Dice ≥ 56.9%). We also compared inter-user variability and obtained Dice coefficients of ≥ 76.8% and ≥ 97.42% for placenta and vasculature, respectively. After a 3-min manual initialization, our GPU approach speeds the computation 10.6 times compared to the CPU., Conclusions: Our semiautomatic method provides a near real-time user experience and requires short training without compromising the segmentation accuracy. A powerful approach is thus presented to rapidly plan the fetoscope insertion point ahead of TTTS surgery.

Fetoscopic laser photocoagulation is the most effective treatment for Twin-to-Twin Transfusion Syndrome, a condition affecting twin pregnancies in which there is a deregulation of blood circulation through the placenta, that can be fatal to both babies. For the purposes of surgical planning, we design the first automatic approach to detect and segment the intrauterine cavity from axial, sagittal and coronal MRI stacks. Our methodology relies on the ability of capsule networks to successfully capture the part-whole interdependency of objects in the scene, particularly for unique class instances (i.e., intrauterine cavity). The presented deep Q-CapsNet reinforcement learning framework is built upon a context-adaptive detection policy to generate a bounding box of the womb. A capsule architecture is subsequently designed to segment (or refine) the whole intrauterine cavity. This network is coupled with a strided nnU-Net feature extractor, which encodes discriminative feature maps to construct strong primary capsules. The method is robustly evaluated with and without the localization stage using 13 performance measures, and directly compared with 15 state-of-the-art deep neural networks trained on 71 singleton and monochorionic twin pregnancies. An average Dice score above 0.91 is achieved for all ablations, revealing the potential of our approach to be used in clinical practice.

Background and Objective: The early identification of malignant pulmonary nodules is critical for a better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules; METHODS: In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection. For training and testing purposes we used independent subsets of the LIDC dataset; RESULTS: Adding the probabilities of nodules malignity in a baseline lung cancer pipeline improved its F1-weighted score by 14.7%, whereas integrating the malignancy model itself using transfer learning outperformed the baseline prediction by 11.8% of F1-weighted score; CONCLUSIONS: Despite the limited size of the lung cancer datasets, integrating predictive models of nodule malignancy improves prediction of lung cancer., Competing Interests: Declaration of Competing Interest The authors confirm that there are no conflicts of interest associated with this publication and there has not been significant financial support for this work that could have influenced its outcome., (Copyright © 2019. Published by Elsevier B.V.)

Twin-to-twin transfusion syndrome (TTTS) is a serious condition that may occur in pregnancies when two or more fetuses share the same placenta. It is characterized by abnormal vascular connections in the placenta that cause blood to flow unevenly between the babies. If left untreated, perinatal mortality occurs in 90% of cases, whilst neurological injuries are still present in TTTS survivors. Minimally invasive fetoscopic laser surgery is the standard and optimal treatment for this condition, but is technically challenging and can lead to complications. Acquiring and maintaining the required surgical skills need consistent practice, and a steep learning curve. An accurate preoperative planning is thus vital for complex TTTS cases. To this end, we propose the first TTTS fetal surgery planning and simulation platform. The soft tissue of the mother, the uterus, the umbilical cords, the placenta and its vascular tree are segmented and registered automatically from magnetic resonance imaging and 3D ultrasound using computer vision and deep learning techniques. The proposed state-of-the-art technology is integrated into a flexible C++ and MITK-based application to provide a full exploration of the intrauterine environment by simulating the fetoscope camera as well as the laser ablation, determining the correct entry point, training doctors' movements and trajectory ahead of operation, which allows improving upon current practice. A comprehensive usability study is reported. Experienced surgeons rated highly our TTTS planner and simulator, thus being a potential tool to be implemented in real and complex TTTS surgeries., (Copyright © 2019. Published by Elsevier B.V.)

Recent advances in fetal magnetic resonance imaging (MRI) open the door to improved detection and characterization of fetal and placental abnormalities. Since interpreting MRI data can be complex and ambiguous, there is a need for robust computational methods able to quantify placental anatomy (including its vasculature) and function. In this work, we propose a novel fully-automated method to segment the placenta and its peripheral blood vessels from fetal MRI. First, a super-resolution reconstruction of the uterus is generated by combining axial, sagittal and coronal views. The placenta is then segmented using 3D Gabor filters, texture features and Support Vector Machines. A uterus edge-based instance selection is proposed to identify the support vectors defining the placenta boundary. Subsequently, peripheral blood vessels are extracted through a curvature-based corner detector. Our approach is validated on a rich set of 44 control and pathological cases: singleton and (normal / monochorionic) twin pregnancies between 25-37 weeks of gestation. Dice coefficients of 0.82  ±  0.02 and 0.81  ±  0.08 are achieved for placenta and its vasculature segmentation, respectively. A comparative analysis with state of the art convolutional neural networks (CNN), namely, 3D U-Net, V-Net, DeepMedic, Holistic3D Net, HighRes3D Net and Dense V-Net is also conducted for placenta localization, with our method outperforming all CNN approaches. Results suggest that our methodology can aid the diagnosis and surgical planning of severe fetal disorders., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Osteoporotic bone fractures reduce quality of life and drastically increase mortality. Minimally irradiating imaging techniques such as dual-energy X-ray absorptiometry (DXA) allow assessment of bone loss through the use of bone mineral density (BMD) as descriptor. Yet, the accuracy of fracture risk predictions remains limited. Recently, DXA-based 3D modelling algorithms were proposed to analyse the geometry and BMD spatial distribution of the proximal femur. This study hypothesizes that such approaches can benefit from finite element (FE)-based biomechanical analyses to improve fracture risk prediction. One hundred and eleven subjects were included in this study and stratified in two groups: (a) 62 fracture cases, and (b) 49 non-fracture controls. Side fall was simulated using a static peak load that depended on patient mass and height. Local mechanical fields were calculated based on relationships between tissue stiffness and BMD. The area under the curve (AUC) of the receiver operating characteristic method evaluated the ability of calculated biomechanical descriptors to discriminate fracture and control cases. The results showed that the major principal stress was better discriminator (AUC > 0.80) than the volumetric BMD (AUC ≤ 0.70). High discrimination capacity was achieved when the analysis was performed by bone type, zone of fracture and gender/sex (AUC of 0.91 for women, trabecular bone and trochanter area), and outcomes suggested that the trabecular bone is critical for fracture discrimination. In conclusion, 3D FE models derived from DXA scans might significantly improve the prediction of hip fracture risk; providing a new insight for clinicians to use FE simulations in clinical practice for osteoporosis management., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Best prenatal diagnosis performances rely on the conscious preparation of the clinicians in terms of fetal anatomy knowledge. Therefore, fetal imaging will likely span and increase its prevalence in the forthcoming years. This review covers state-of-the-art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time. Potential applications of the aforementioned methods into clinical settings are also inspected. Finally, improvements in existing approaches as well as most promising avenues to new areas of research are briefly outlined., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Machine learning approaches for image analysis require large amounts of training imaging data. As an alternative, the use of realistic synthetic data reduces the high cost associated to medical image acquisition, as well as avoiding confidentiality and privacy issues, and consequently allows the creation of public data repositories for scientific purposes. Within the context of fetal imaging, we adopt an auto-encoder based Generative Adversarial Network for synthetic fetal MRI generation. The proposed architecture features a balanced power of the discriminator against the generator during training, provides an approximate convergence measure, and enables fast and robust training to generate high-quality fetal MRI in axial, sagittal and coronal planes. We demonstrate the feasibility of the proposed approach quantitatively and qualitatively by segmenting relevant fetal structures to assess the anatomical fidelity of the simulation, and performing a clinical verisimilitude study distinguishing the simulated data from the real images. The results obtained so far are promising, which makes further investigation on this new topic worthwhile.

Cochlear implantation (CI) is a complex surgical procedure that restores hearing in patients with severe deafness. The successful outcome of the implanted device relies on a group of factors, some of them unpredictable or difficult to control. Uncertainties on the electrode array position and the electrical properties of the bone make it difficult to accurately compute the current propagation delivered by the implant and the resulting neural activation. In this context, we use uncertainty quantification methods to explore how these uncertainties propagate through all the stages of CI computational simulations. To this end, we employ an automatic framework, encompassing from the finite element generation of CI models to the assessment of the neural response induced by the implant stimulation. To estimate the confidence intervals of the simulated neural response, we propose two approaches. First, we encode the variability of the cochlear morphology among the population through a statistical shape model. This allows us to generate a population of virtual patients using Monte Carlo sampling and to assign to each of them a set of parameter values according to a statistical distribution. The framework is implemented and parallelized in a High Throughput Computing environment that enables to maximize the available computing resources. Secondly, we perform a patient-specific study to evaluate the computed neural response to seek the optimal post-implantation stimulus levels. Considering a single cochlear morphology, the uncertainty in tissue electrical resistivity and surgical insertion parameters is propagated using the Probabilistic Collocation method, which reduces the number of samples to evaluate. Results show that bone resistivity has the highest influence on CI outcomes. In conjunction with the variability of the cochlear length, worst outcomes are obtained for small cochleae with high resistivity values. However, the effect of the surgical insertion length on the CI outcomes could not be clearly observed, since its impact may be concealed by the other considered parameters. Whereas the Monte Carlo approach implies a high computational cost, Probabilistic Collocation presents a suitable trade-off between precision and computational time. Results suggest that the proposed framework has a great potential to help in both surgical planning decisions and in the audiological setting process.

Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Chronic Obstructive Pulmonary Disease (COPD) is a disabling respiratory pathology, with a high prevalence and a significant economic and social cost. It is characterized by different clinical phenotypes with different risk profiles. Detecting the correct phenotype, especially for the emphysema subtype, and predicting the risk of major exacerbations are key elements in order to deliver more effective treatments. However, emphysema onset and progression are influenced by a complex interaction between the immune system and the mechanical properties of biological tissue. The former causes chronic inflammation and tissue remodeling. The latter influences the effective resistance or appropriate mechanical response of the lung tissue to repeated breathing cycles. In this work we present a multi-scale model of both aspects, coupling Finite Element (FE) and Agent Based (AB) techniques that we would like to use to predict the onset and progression of emphysema in patients. The AB part is based on existing biological models of inflammation and immunological response as a set of coupled non-linear differential equations. The FE part simulates the biomechanical effects of repeated strain on the biological tissue. We devise a strategy to couple the discrete biological model at the molecular /cellular level and the biomechanical finite element simulations at the tissue level. We tested our implementation on a public emphysema image database and found that it can indeed simulate the evolution of clinical image biomarkers during disease progression.

Purpose: A personalized estimation of the cochlear shape can be used to create computational anatomical models to aid cochlear implant (CI) surgery and CI audio processor programming ultimately resulting in improved hearing restoration. The purpose of this work is to develop and test a method for estimation of the detailed patient-specific cochlear shape from CT images., Methods: From a collection of temporal bone [Formula: see text]CT images, we build a cochlear statistical deformation model (SDM), which is a description of how a human cochlea deforms to represent the observed anatomical variability. The model is used for regularization of a non-rigid image registration procedure between a patient CT scan and a [Formula: see text]CT image, allowing us to estimate the detailed patient-specific cochlear shape., Results: We test the accuracy and precision of the predicted cochlear shape using both [Formula: see text]CT and CT images. The evaluation is based on classic generic metrics, where we achieve competitive accuracy with the state-of-the-art methods for the task. Additionally, we expand the evaluation with a few anatomically specific scores., Conclusions: The paper presents the process of building and using the SDM of the cochlea. Compared to current best practice, we demonstrate competitive performance and some useful properties of our method.

Cochlear implantation (CI) surgery is a very successful technique, performed on more than 300,000 people worldwide. However, since the challenge resides in obtaining an accurate surgical planning, computational models are considered to provide such accurate tools. They allow us to plan and simulate beforehand surgical procedures in order to maximally optimize surgery outcomes, and consequently provide valuable information to guide pre-operative decisions. The aim of this work is to develop and validate computational tools to completely assess the patient-specific functional outcome of the CI surgery. A complete automatic framework was developed to create and assess computationally CI models, focusing on the neural response of the auditory nerve fibers (ANF) induced by the electrical stimulation of the implant. The framework was applied to evaluate the effects of ANF degeneration and electrode intra-cochlear position on nerve activation. Results indicate that the intra-cochlear positioning of the electrode has a strong effect on the global performance of the CI. Lateral insertion provides better neural responses in case of peripheral process degeneration, and it is recommended, together with optimized intensity levels, in order to preserve the internal structures. Overall, the developed automatic framework provides an insight into the global performance of the implant in a patient-specific way. This enables to further optimize the functional performance and helps to select the best CI configuration and treatment strategy for a given patient.

Understanding the human inner ear anatomy and its internal structures is paramount to advance hearing implant technology. While the emergence of imaging devices allowed researchers to improve understanding of intracochlear structures, the difficulties to collect appropriate data has resulted in studies conducted with few samples. To assist the cochlear research community, a large collection of human temporal bone images is being made available. This data descriptor, therefore, describes a rich set of image volumes acquired using cone beam computed tomography and micro-CT modalities, accompanied by manual delineations of the cochlea and sub-compartments, a statistical shape model encoding its anatomical variability, and data for electrode insertion and electrical simulations. This data makes an important asset for future studies in need of high-resolution data and related statistical data objects of the cochlea used to leverage scientific hypotheses. It is of relevance to anatomists, audiologists, computer scientists in the different domains of image analysis, computer simulations, imaging formation, and for biomedical engineers designing new strategies for cochlear implantations, electrode design, and others.

Experimental studies on isolated cardiomyocytes from different animal species and human hearts have demonstrated that there are regional differences in the Ca2+ release, Ca2+ decay and sarcomere deformation. Local deformation heterogeneities can occur due to a combination of factors: regional/local differences in Ca2+ release and/or re-uptake, intra-cellular material properties, sarcomere proteins and distribution of the intracellular organelles. To investigate the possible causes of these heterogeneities, we developed a two-dimensional finite-element electromechanical model of a cardiomyocyte that takes into account the experimentally measured local deformation and cytosolic [Ca2+] to locally define the different variables of the constitutive equations describing the electro/mechanical behaviour of the cell. Then, the model was individualised to three different rat cardiac cells. The local [Ca2+] transients were used to define the [Ca2+]-dependent activation functions. The cell-specific local Young's moduli were estimated by solving an inverse problem, minimizing the error between the measured and simulated local deformations along the longitudinal axis of the cell. We found that heterogeneities in the deformation during contraction were determined mainly by the local elasticity rather than the local amount of Ca2+, while in the relaxation phase deformation was mainly influenced by Ca2+ re-uptake. Our electromechanical model was able to successfully estimate the local elasticity along the longitudinal direction in three different cells. In conclusion, our proposed model seems to be a good approximation to assess the heterogeneous intracellular mechanical properties to help in the understanding of the underlying mechanisms of cardiomyocyte dysfunction.

This work presents a multi-scale agent-based model of emphysema progression that includes both the slow action of the immune system and the fast action of force redistribution and fracture propagation of the biological tissue. The two scales are coupled because the immune response causes inflammation and adaptation, which affects the biomechanical parameters of the tissue such as his elasticity. During repeated inflammation and breathing cycles, the tissue weakens and breaks down. We found that macrophages lifespan and cytokynes diffusion ratio are the parameters that influence the outcome of the model the most.

Background: The association between deep vein thrombosis (DVT) and atherosclerosis is still controversial., Methods: We examined the rate of subsequent symptomatic atherosclerosis in patients with unprovoked as compared to secondary DVT with a retrospectively follow-up of a cohort of patients who 14 years earlier had developed an episode of DVT not preceded by arterial cardiovascular events. We collected information on the development of coronary heart disease, ischemic stroke, peripheral artery disease or sudden otherwise unexplained death., Results: We retrieved information from 138 patients with unprovoked and 123 with secondary DVT. The cumulative incidence of symptomatic atherosclerosis was 17.6% (95% CI, 8.3 to 26.0) in patients with unprovoked DVT, and 5.1% (95% CI: 0.0 to 10.7) in those with secondary DVT. After adjusting for age, sex, smoking, hypertension, diabetes and dyslipidemia, the hazard ratio (HR) for development of symptomatic atherosclerosis among patients with unprovoked DVT as compared to those with secondary DVT was 2.89 (95% CI, 1.06 to 7.88; P=0.038)., Conclusions: The risk of subsequent symptomatic atherosclerosis among patients with unprovoked DVT is approximately three times as high as that of patients with secondary events.

Purpose: Cochlear implantation is a safe and effective surgical procedure to restore hearing in deaf patients. However, the level of restoration achieved may vary due to differences in anatomy, implant type and surgical access. In order to reduce the variability of the surgical outcomes, we previously proposed the use of a high-resolution model built from [Formula: see text] images and then adapted to patient-specific clinical CT scans. As the accuracy of the model is dependent on the precision of the original segmentation, it is extremely important to have accurate [Formula: see text] segmentation algorithms., Methods: We propose a new framework for cochlea segmentation in ex vivo [Formula: see text] images using random walks where a distance-based shape prior is combined with a region term estimated by a Gaussian mixture model. The prior is also weighted by a confidence map to adjust its influence according to the strength of the image contour. Random walks is performed iteratively, and the prior mask is aligned in every iteration., Results: We tested the proposed approach in ten [Formula: see text] data sets and compared it with other random walks-based segmentation techniques such as guided random walks (Eslami et al. in Med Image Anal 17(2):236-253, 2013) and constrained random walks (Li et al. in Advances in image and video technology. Springer, Berlin, pp 215-226, 2012). Our approach demonstrated higher accuracy results due to the probability density model constituted by the region term and shape prior information weighed by a confidence map., Conclusion: The weighted combination of the distance-based shape prior with a region term into random walks provides accurate segmentations of the cochlea. The experiments suggest that the proposed approach is robust for cochlea segmentation.

Recent developments in computational modeling of cochlear implantation are promising to study in silico the performance of the implant before surgery. However, creating a complete computational model of the patient's anatomy while including an external device geometry remains challenging. To address such a challenge, we propose an automatic framework for the generation of patient-specific meshes for finite element modeling of the implanted cochlea. First, a statistical shape model is constructed from high-resolution anatomical μCT images. Then, by fitting the statistical model to a patient's CT image, an accurate model of the patient-specific cochlea anatomy is obtained. An algorithm based on the parallel transport frame is employed to perform the virtual insertion of the cochlear implant. Our automatic framework also incorporates the surrounding bone and nerve fibers and assigns constitutive parameters to all components of the finite element model. This model can then be used to study in silico the effects of the electrical stimulation of the cochlear implant. Results are shown on a total of 25 models of patients. In all cases, a final mesh suitable for finite element simulations was obtained, in an average time of 94 s. The framework has proven to be fast and robust, and is promising for a detailed prognosis of the cochlear implantation surgery.

Electrical stimulation of the brain has resulted in the most successful neuroprosthetic techniques to date: deep brain stimulation (DBS) and cochlear implants (CI). In both cases, there is a lack of pre-operative measures to predict the outcomes after implantation. We argue that highly detailed computational models that are specifically tailored for a patient can provide useful information to improve the precision of the nervous system electrode interface. We apply our framework to the case of CI, showing how we can predict nerve response for patients with both intact and degenerated nerve fibers. Then, using the predicted response, we calculate a metric for the usefulness of the stimulation protocol and use this information to rerun the simulations with better parameters.