Metabolic-associated fatty liver voxel-based quantification on CT images using a contrast adapted automatic tool.

BACKGROUND & AIMS: Metabolic-dysfunction associated fatty liver disease (MAFLD) is highly prevalent and can lead to liver complications and comorbidities, with non-invasive tests such as vibration-controlled transient elastography (VCTE) and invasive liver biopsies being used for diagnosis The aim of the present study was to develop a new fully automatized method for quantifying the percentage of fat in the liver based on a voxel analysis on computed tomography (CT) images to solve previously unconcluded diagnostic deficiencies either in contrast (CE) or non-contrast enhanced (NCE) assessments. METHODS: Liver and spleen were segmented using nn-UNet on CE- and NCE-CT images. Radiodensity values were obtained for both organs for defining the key benchmarks for fatty liver assessment: liver mean, liver-to-spleen ratio, liver-spleen difference, and their average. VCTE was used for validation. A classification task method was developed for detection of suitable patients to fulfill maximum reproducibility across cohorts and highlight subjects with other potential radiodensity-related diseases. RESULTS: Best accuracy was attained using the average of all proposed benchmarks being the liver-to-spleen ratio highly useful for CE and the liver-to-spleen difference for NCE. The proposed whole-organ automatic segmentation displayed superior potential when compared to the typically used manual region-of-interest drawing as it allows to accurately obtain the percent of fat in liver, among other improvements. Atypical patients were successfully stratified through a function based on biochemical data. CONCLUSIONS: The developed method tackles the current drawbacks including biopsy invasiveness, and CT-related weaknesses such as lack of automaticity, dependency on contrast agent, no quantification of the percentage of fat in liver, and limited information on region-to-organ affectation. We propose this tool as an alternative for individualized MAFLD evaluation by an early detection of abnormal CT patterns based in radiodensity whilst abording detection of non-suitable patients to avoid unnecessary exposure to CT radiation. Furthermore, this work presents a surrogate aid for assessing fatty liver at a primary assessment of MAFLD using elastography data.

Dataset of Finite Element Models of Normal and Deformed Thoracolumbar Spine.

Adult spine deformity (ASD) is prevalent and leads to a sagittal misalignment in the vertebral column. Computational methods, including Finite Element (FE) Models, have emerged as valuable tools for investigating the causes and treatment of ASD through biomechanical simulations. However, the process of generating personalised FE models is often complex and time-consuming. To address this challenge, we present a dataset of FE models with diverse spine morphologies that statistically represent real geometries from a cohort of patients. These models are generated using EOS images, which are utilized to reconstruct 3D surface spine models. Subsequently, a Statistical Shape Model (SSM) is constructed, enabling the adaptation of a FE hexahedral mesh template for both the bone and soft tissues of the spine through mesh morphing. The SSM deformation fields facilitate the personalization of the mean hexahedral FE model based on sagittal balance measurements. Ultimately, this new hexahedral SSM tool offers a means to generate a virtual cohort of 16807 thoracolumbar FE spine models, which are openly shared in a public repository.

Proximal Junction Failure in Spine Surgery: Integrating Geometrical and Biomechanical Global Descriptors Improves GAP Score-Based Assessment.

STUDY DESIGN: Retrospective observational study. OBJECTIVE: Biomechanical and geometrical descriptors are used to improve global alignment and proportion (GAP) prediction accuracy to detect proximal junctional failure (PJF). SUMMARY OF BACKGROUND DATA: PJF is probably the most important complication after sagittal imbalance surgery. The GAP score has been introduced as an effective predictor for PJF, but it fails in certain situations. In this study, 112 patient records were gathered (57 PJF; 55 controls) with biomechanical and geometrical descriptors measured to stratify control and failure cases. PATIENTS AND METHODS: Biplanar EOS radiographs were used to build 3-dimensional full-spine models and determine spinopelvic sagittal parameters. The bending moment (BM) was calculated as the upper body mass times, the effective distance to the body center of mass at the adjacent upper instrumented vertebra +1. Other geometrical descriptors such as full balance index (FBI), spino-sacral angle (SSA), C7 plumb line/sacrofemoral distance ratio (C7/SFD ratio), T1-pelvic angle (TPA), and cervical inclination angle (CIA) were also evaluated. The respective abilities of the GAP, FBI, SSA, C7/SFD, TPA, CIA, body weight, body mass index, and BM to discriminate PJF cases were analyzed through receiver operating characteristic curves and corresponding areas under the curve (AUC). RESULTS: GAP (AUC = 0.8816) and FBI (AUC = 0.8933) were able to discriminate PJF cases but the highest discrimination power (AUC = 0.9371) was achieved with BM at upper instrumented vertebra + 1. Parameter cutoff analyses provided quantitative thresholds to characterize the control and failure groups and led to improved PJF discrimination, with GAP and BM being the most important contributors. SSA (AUC = 0.2857), C7/SFD (AUC = 0.3143), TPA (AUC = 0.5714), CIA (AUC = 0.4571), body weight (AUC = 0.6319), and body mass index (AUC = 0.7716) did not adequately predict PJF. CONCLUSION: BM reflects the quantitative biomechanical effect of external loads and can improve GAP accuracy. Sagittal alignments and mechanical integrated scores could be used to better prognosticate the risk of PJF.

Insights into Insulin Resistance and Calcification in the Myocardium in Type 2 Diabetes: A Coronary Artery Analysis.

Type 2 diabetes (T2D) is responsible for high incidence of cardiovascular (CV) complications leading to heart failure. Coronary artery region-specific metabolic and structural assessment could provide deeper insight into the extent of the disease and help prevent adverse cardiac events. Therefore, in this study, we aimed at investigating such myocardial dynamics for the first time in insulin-sensitive (mIS) and insulin-resistant (mIR) T2D patients. We targeted global and region-specific variations using insulin sensitivity (IS) and coronary artery calcifications (CACs) as CV risk factor in T2D patients. IS was computed using myocardial segmentation approaches at both baseline and after an hyperglycemic-insulinemic clamp (HEC) on [18F]FDG-PET images using the standardized uptake value (SUV) (ΔSUV = SUVHEC - SUVBASELINE) and calcifications using CT Calcium Scoring. Results suggest that some communicating pathways between response to insulin and calcification are present in the myocardium, whilst differences between coronary arteries were only observed in the mIS cohort. Risk indicators were mostly observed for mIR and highly calcified subjects, which supports previously stated findings that exhibit a distinguished exposure depending on the impairment of response to insulin, while projecting added potential complications due to arterial obstruction. Moreover, a pattern relating calcification and T2D phenotypes was observed suggesting the avoidance of insulin treatment in mIS but its endorsement in mIR subjects. The right coronary artery displayed more ΔSUV, whilst plaque was more present in the circumflex. However, differences between phenotypes, and therefore CV risk, were associated to left descending artery (LAD) translating into higher CACs regarding IR, which could explain why insulin treatment was effective for LAD at the expense of higher likelihood of plaque accumulation. Personalized approaches to assess T2D may lead to more efficient treatments and risk-prevention strategies.

An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.

BACKGROUND AND OBJECTIVE: The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging. METHODS: In this work, a new pipeline for fetal and neonatal segmentation has been developed. We also report the creation of two new fetal atlases, and their use within the pipeline for atlas-based segmentation, based on novel registration methods. The pipeline is also able to extract cortical and pial surfaces and compute features, such as curvature, local gyrification index, sulcal depth, and thickness. RESULTS: Results show that the introduction of the new templates together with our segmentation strategy leads to accurate results when compared to expert annotations, as well as better performances when compared to a reference pipeline (developing Human Connectome Project (dHCP)), for both early and late-onset fetal brains. CONCLUSIONS: These findings show the potential of the presented atlases and the whole pipeline for application in both fetal, neonatal, and longitudinal studies, which could lead to dramatic improvements in the understanding of perinatal brain development.

Association of central obesity with unique cardiac remodelling in young adults born small for gestational age.

AIMS: Being born small for gestational age (SGA, 10% of all births) is associated with increased risk of cardiovascular mortality in adulthood together with lower exercise tolerance, but mechanistic pathways are unclear. Central obesity is known to worsen cardiovascular outcomes, but it is uncertain how it affects the heart in adults born SGA. We aimed to assess whether central obesity makes young adults born SGA more susceptible to cardiac remodelling and dysfunction. METHODS AND RESULTS: A perinatal cohort from a tertiary university hospital in Spain of young adults (30-40 years) randomly selected, 80 born SGA (birth weight below 10th centile) and 75 with normal birth weight (controls) was recruited. We studied the associations between SGA and central obesity (measured via the hip-to-waist ratio and used as a continuous variable) and cardiac regional structure and function, assessed by cardiac magnetic resonance using statistical shape analysis. Both SGA and waist-to-hip were highly associated to cardiac shape (F = 3.94, P < 0.001; F = 5.18, P < 0.001 respectively) with a statistically significant interaction (F = 2.29, P = 0.02). While controls tend to increase left ventricular end-diastolic volumes, mass and stroke volume with increasing waist-to-hip ratio, young adults born SGA showed a unique response with inability to increase cardiac dimensions or mass resulting in reduced stroke volume and exercise capacity. CONCLUSION: SGA young adults show a unique cardiac adaptation to central obesity. These results support considering SGA as a risk factor that may benefit from preventive strategies to reduce cardiometabolic risk.

Attri-VAE: Attribute-based interpretable representations of medical images with variational autoencoders.

Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus facilitating their use in the reasoning behind the medical decisions. Latent space representations built with variational autoencoders (VAE) do not ensure individual control of data attributes. Attribute-based methods enforcing attribute disentanglement have been proposed in the literature for classical computer vision tasks in benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that includes an attribute regularization term to associate clinical and medical imaging attributes with different regularized dimensions in the generated latent space, enabling a better-disentangled interpretation of the attributes. Furthermore, the generated attention maps explained the attribute encoding in the regularized latent space dimensions. Using the Attri-VAE approach we analyzed healthy and myocardial infarction patients with clinical, cardiac morphology, and radiomics attributes. The proposed model provided an excellent trade-off between reconstruction fidelity, disentanglement, and interpretability, outperforming state-of-the-art VAE approaches according to several quantitative metrics. The resulting latent space allowed the generation of realistic synthetic data in the trajectory between two distinct input samples or along a specific attribute dimension to better interpret changes between different cardiac conditions.

Memory-aware curriculum federated learning for breast cancer classification.

BACKGROUND AND OBJECTIVE: For early breast cancer detection, regular screening with mammography imaging is recommended. Routine examinations result in datasets with a predominant amount of negative samples. The limited representativeness of positive cases can be problematic for learning Computer-Aided Diagnosis (CAD) systems. Collecting data from multiple institutions is a potential solution to mitigate this problem. Recently, federated learning has emerged as an effective tool for collaborative learning. In this setting, local models perform computation on their private data to update the global model. The order and the frequency of local updates influence the final global model. In the context of federated adversarial learning to improve multi-site breast cancer classification, we investigate the role of the order in which samples are locally presented to the optimizers. METHODS: We define a novel memory-aware curriculum learning method for the federated setting. We aim to improve the consistency of the local models penalizing inconsistent predictions, i.e., forgotten samples. Our curriculum controls the order of the training samples prioritizing those that are forgotten after the deployment of the global model. Our approach is combined with unsupervised domain adaptation to deal with domain shift while preserving data privacy. RESULTS: Two classification metrics: area under the receiver operating characteristic curve (ROC-AUC) and area under the curve for the precision-recall curve (PR-AUC) are used to evaluate the performance of the proposed method. Our method is evaluated with three clinical datasets from different vendors. An ablation study showed the improvement of each component of our method. The AUC and PR-AUC are improved on average by 5% and 6%, respectively, compared to the conventional federated setting. CONCLUSIONS: We demonstrated the benefits of curriculum learning for the first time in a federated setting. Our results verified the effectiveness of the memory-aware curriculum federated learning for the multi-site breast cancer classification. Our code is publicly available at: https://github.com/ameliajimenez/curriculum-federated-learning.

Regulatory network-based model to simulate the biochemical regulation of chondrocytes in healthy and osteoarthritic environments.

In osteoarthritis (OA), chondrocyte metabolism dysregulation increases relative catabolic activity, which leads to cartilage degradation. To enable the semiquantitative interpretation of the intricate mechanisms of OA progression, we propose a network-based model at the chondrocyte level that incorporates the complex ways in which inflammatory factors affect structural protein and protease expression and nociceptive signals. Understanding such interactions will leverage the identification of new potential therapeutic targets that could improve current pharmacological treatments. Our computational model arises from a combination of knowledge-based and data-driven approaches that includes in-depth analyses of evidence reported in the specialized literature and targeted network enrichment. We achieved a mechanistic network of molecular interactions that represent both biosynthetic, inflammatory and degradative chondrocyte activity. The network is calibrated against experimental data through a genetic algorithm, and 81% of the responses tested have a normalized root squared error lower than 0.15. The model captures chondrocyte-reported behaviors with 95% accuracy, and it correctly predicts the main outcomes of OA treatment based on blood-derived biologics. The proposed methodology allows us to model an optimal regulatory network that controls chondrocyte metabolism based on measurable soluble molecules. Further research should target the incorporation of mechanical signals.

Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching.

Automated anatomical vessel labeling of the abdominal arterial system is a crucial topic in medical image processing. One reason for this is the importance of the abdominal arterial system in the human body, and another is that such labeling is necessary for the related disease diagnoses, treatments and epidemiological population analyses. We define a hypergraph representation of the abdominal arterial system as a family tree model with a probabilistic hypergraph matching framework for automated vessel labeling. Then we treat the labelling problem as the convex optimization problem and solve it with the maximum a posteriori(MAP) combined the likelihood obtained by geometric labelling with the family tree topology-based knowledge. Geometrically, we utilize XGBoost ensemble learning with an intrinsic geometric feature importance analysis for branch-level labeling. In topology, the defined family tree model of the abdominal arterial system is transferred as a Markov chain model using a constrained traversal order method and further the Markov chain model is optimized by a hidden Markov model (HMM). The probability distribution of the target branches for each candidate anatomical name is predicted and effectively embedded in the HMM model. This approach is evaluated with the leave-one-out method on 37 clinical patients' abdominal arteries, and the average accuracy is 91.94%. The obtained results are better than those of the state-of-art method with an F1 score of 93.00% and a recall of 93.00%, as the proposed method simultaneously handles the anatomical structural variability and discriminates between the symmetric branches. It is demonstrated to be suitable for labelling branches of the abdominal arterial system and can also be extended to similar tubular organ networks, such as arterial or airway networks.

Curriculum learning for improved femur fracture classification: Scheduling data with prior knowledge and uncertainty.

An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients' clinical outcome. We rely on the commonly used AO system, which describes a hierarchical knowledge tree classifying the images into types and subtypes according to the fracture's location and complexity. In this paper, we propose a method for the automatic classification of proximal femur fractures into 3 and 7 AO classes based on a Convolutional Neural Network (CNN). As it is known, CNNs need large and representative datasets with reliable labels, which are hard to collect for the application at hand. In this paper, we design a curriculum learning (CL) approach that improves over the basic CNNs performance under such conditions. Our novel formulation reunites three curriculum strategies: individually weighting training samples, reordering the training set, and sampling subsets of data. The core of these strategies is a scoring function ranking the training samples. We define two novel scoring functions: one from domain-specific prior knowledge and an original self-paced uncertainty score. We perform experiments on a clinical dataset of proximal femur radiographs. The curriculum improves proximal femur fracture classification up to the performance of experienced trauma surgeons. The best curriculum method reorders the training set based on prior knowledge resulting into a classification improvement of 15%. Using the publicly available MNIST dataset, we further discuss and demonstrate the benefits of our unified CL formulation for three controlled and challenging digit recognition scenarios: with limited amounts of data, under class-imbalance, and in the presence of label noise. The code of our work is available at: https://github.com/ameliajimenez/curriculum-learning-prior-uncertainty.

Exercise Capacity in Young Adults Born Small for Gestational Age.

Importance: Being born small for gestational age (SGA), approximately 10% of all births, is associated with increased risk of cardiovascular mortality in adulthood, but mechanistic pathways are unclear. Cardiac remodeling and dysfunction occur in fetuses SGA and children born SGA, but it is uncertain whether and how these changes persist into adulthood. Objective: To evaluate baseline cardiac function and structure and exercise capacity in young adults born SGA. Design, Setting, and Participants: This cohort study conducted from January 2015 to January 2018 assessed a perinatal cohort born at a tertiary university hospital in Spain between 1975 and 1995. Participants included 158 randomly selected young adults aged 20 to 40 years born SGA (birth weight below the 10th centile) or with intrauterine growth within standard reference ranges (controls). Participants provided their medical history, filled out questionnaires regarding smoking and physical activity habits, and underwent incremental cardiopulmonary exercise stress testing, cardiac magnetic resonance imaging, and a physical examination, with blood pressure, glucose level, and lipid profile data collected. Exposure: Being born SGA. Main Outcomes and Measures: Cardiac structure and function assessed by cardiac magnetic resonance imaging, including biventricular end-diastolic shape analysis. Exercise capacity assessed by incremental exercise stress testing. Results: This cohort study included 81 adults born SGA (median age at study, 34.4 years [IQR, 30.8-36.7 years]; 43 women [53%]) and 77 control participants (median age at study, 33.7 years [interquartile range (IQR), 31.0-37.1 years]; 33 women [43%]). All participants were of White race/ethnicity and underwent imaging, whereas 127 participants (80% of the cohort; 66 control participants and 61 adults born SGA) completed the exercise test. Cardiac shape analysis showed minor changes at rest in right ventricular geometry (DeLong test z, 2.2098; P = .02) with preserved cardiac function in individuals born SGA. However, compared with controls, adults born SGA had lower exercise capacity, with decreased maximal workload (mean [SD], 180 [62] W vs 214 [60] W; P = .006) and oxygen consumption (median, 26.0 mL/min/kg [IQR, 21.5-33.5 mL/min/kg vs 29.5 mL/min/kg [IQR, 24.0-36.0 mL/min/kg]; P = .02). Exercise capacity was significantly correlated with left ventricular mass (ρ = 0.7934; P < .001). Conclusions and Relevance: This cohort of young adults born SGA had markedly reduced exercise capacity. These results support further research to clarify the causes of impaired exercise capacity and the potential association with increased cardiovascular mortality among adults born SGA.

Identifying causative mechanisms linking early-life stress to psycho-cardio-metabolic multi-morbidity: The EarlyCause project.

INTRODUCTION: Depression, cardiovascular diseases and diabetes are among the major non-communicable diseases, leading to significant disability and mortality worldwide. These diseases may share environmental and genetic determinants associated with multimorbid patterns. Stressful early-life events are among the primary factors associated with the development of mental and physical diseases. However, possible causative mechanisms linking early life stress (ELS) with psycho-cardio-metabolic (PCM) multi-morbidity are not well understood. This prevents a full understanding of causal pathways towards the shared risk of these diseases and the development of coordinated preventive and therapeutic interventions. METHODS AND ANALYSIS: This paper describes the study protocol for EarlyCause, a large-scale and inter-disciplinary research project funded by the European Union's Horizon 2020 research and innovation programme. The project takes advantage of human longitudinal birth cohort data, animal studies and cellular models to test the hypothesis of shared mechanisms and molecular pathways by which ELS shapes an individual's physical and mental health in adulthood. The study will research in detail how ELS converts into biological signals embedded simultaneously or sequentially in the brain, the cardiovascular and metabolic systems. The research will mainly focus on four biological processes including possible alterations of the epigenome, neuroendocrine system, inflammatome, and the gut microbiome. Life-course models will integrate the role of modifying factors as sex, socioeconomics, and lifestyle with the goal to better identify groups at risk as well as inform promising strategies to reverse the possible mechanisms and/or reduce the impact of ELS on multi-morbidity development in high-risk individuals. These strategies will help better manage the impact of multi-morbidity on human health and the associated risk.

Multiscale Regulation of the Intervertebral Disc: Achievements in Experimental, In Silico, and Regenerative Research.

Intervertebral disc (IVD) degeneration is a major risk factor of low back pain. It is defined by a progressive loss of the IVD structure and functionality, leading to severe impairments with restricted treatment options due to the highly demanding mechanical exposure of the IVD. Degenerative changes in the IVD usually increase with age but at an accelerated rate in some individuals. To understand the initiation and progression of this disease, it is crucial to identify key top-down and bottom-up regulations' processes, across the cell, tissue, and organ levels, in health and disease. Owing to unremitting investigation of experimental research, the comprehension of detailed cell signaling pathways and their effect on matrix turnover significantly rose. Likewise, in silico research substantially contributed to a holistic understanding of spatiotemporal effects and complex, multifactorial interactions within the IVD. Together with important achievements in the research of biomaterials, manifold promising approaches for regenerative treatment options were presented over the last years. This review provides an integrative analysis of the current knowledge about (1) the multiscale function and regulation of the IVD in health and disease, (2) the possible regenerative strategies, and (3) the in silico models that shall eventually support the development of advanced therapies.

Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound.

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.

On the Use of Digital Subtraction Angiography in Stereoelectroencephalography Surgical Planning to Prevent Collisions with Vessels.

OBJECTIVE: Stereoelectroencephalography (SEEG) consists of the implantation of microelectrodes for the electrophysiological characterization of epileptogenic networks. To reduce a possible risk of intracranial bleeding by vessel rupture during the electrode implantation, the stereotactic trajectories must follow avascular corridors. The use of digital subtraction angiography (DSA) for vascular visualization during planning is controversial due to the additional risk related to this procedure. Here we evaluate the utility of this technique for planning when the neurosurgeon has it available together with gadolinium-enhanced T1-weighted magnetic resonance sequence (T1-Gd) and computed tomography angiography (CTA). METHODS: Twenty-two implantation plans for SEEG were initially done using T1-Gd imaging (251 trajectories). DSA was only used later during the revision process. In 6 patients CTA was available at this point as well. We quantified the position of the closest vessel to the trajectory in each of the imaging modalities. RESULTS: Two thirds of the trajectories that appeared vessel free in the T1-Gd or CTA presented vessels in their proximity, as shown by DSA. Those modifications only required small shifts of both the entry and target point, so the diagnostic aims were preserved. CONCLUSIONS: T1-Gd and CTA, despite being the most commonly used techniques for SEEG planning, frequently fail to reveal vessels that are dangerously close to the trajectories. Higher-resolution vascular imaging techniques, such as DSA, can provide the neurosurgeon with crucial information about vascular anatomy, resulting in safer plans.

Re-Identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks.

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.

Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank.

Cardiovascular magnetic resonance (CMR) radiomics is a novel technique for advanced cardiac image phenotyping by analyzing multiple quantifiers of shape and tissue texture. In this paper, we assess, in the largest sample published to date, the performance of CMR radiomics models for identifying changes in cardiac structure and tissue texture due to cardiovascular risk factors. We evaluated five risk factor groups from the first 5,065 UK Biobank participants: hypertension (n = 1,394), diabetes (n = 243), high cholesterol (n = 779), current smoker (n = 320), and previous smoker (n = 1,394). Each group was randomly matched with an equal number of healthy comparators (without known cardiovascular disease or risk factors). Radiomics analysis was applied to short axis images of the left and right ventricles at end-diastole and end-systole, yielding a total of 684 features per study. Sequential forward feature selection in combination with machine learning (ML) algorithms (support vector machine, random forest, and logistic regression) were used to build radiomics signatures for each specific risk group. We evaluated the degree of separation achieved by the identified radiomics signatures using area under curve (AUC), receiver operating characteristic (ROC), and statistical testing. Logistic regression with L1-regularization was the optimal ML model. Compared to conventional imaging indices, radiomics signatures improved the discrimination of risk factor vs. healthy subgroups as assessed by AUC [diabetes: 0.80 vs. 0.70, hypertension: 0.72 vs. 0.69, high cholesterol: 0.71 vs. 0.65, current smoker: 0.68 vs. 0.65, previous smoker: 0.63 vs. 0.60]. Furthermore, we considered clinical interpretation of risk-specific radiomics signatures. For hypertensive individuals and previous smokers, the surface area to volume ratio was smaller in the risk factor vs. healthy subjects; perhaps reflecting a pattern of global concentric hypertrophy in these conditions. In the diabetes subgroup, the most discriminatory radiomics feature was the median intensity of the myocardium at end-systole, which suggests a global alteration at the myocardial tissue level. This study confirms the feasibility and potential of CMR radiomics for deeper image phenotyping of cardiovascular health and disease. We demonstrate such analysis may have utility beyond conventional CMR metrics for improved detection and understanding of the early effects of cardiovascular risk factors on cardiac structure and tissue.

Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning.

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.