863 results found
<jats:p>The increasing prevalence of large-scale data collection in modern society represents a potential threat to individual privacy. Addressing this threat, for example through privacy-enhancing technologies (PETs), requires a rigorous definition of what exactly is being protected, that is, of privacy itself. In this work, we formulate an axiomatic definition of privacy based on quantifiable and irreducible information flows. Our definition synthesizes prior work from the domain of social science with a contemporary understanding of PETs such as differential privacy (DP). Our work highlights the fact that the inevitable difficulties of protecting privacy in practice are fundamentally information-theoretic. Moreover, it enables quantitative reasoning about PETs based on what they are protecting, thus fostering objective policy discourse about their societal implementation.</jats:p>
Taoudi-Benchekroun Y, Christiaens D, Grigorescu I, et al., 2022, Predicting age and clinical risk from the neonatal connectome, NeuroImage, Pages: 119319-119319, ISSN: 1053-8119
Dou Q, So TY, Jiang M, et al., 2022, Y Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study (vol 5, 56, 2022), NPJ DIGITAL MEDICINE, Vol: 5, ISSN: 2398-6352
Lachmann M, Rippen E, Rueckert D, et al., 2022, Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis, European Heart Journal - Digital Health
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Aims</jats:title> <jats:p>Hypothesizing that aortic outflow velocity profiles contain more valuable information about aortic valve obstruction and left ventricular contractility than can be captured by the human eye, features of the complex geometry of Doppler tracings from patients with severe aortic stenosis (AS) were extracted by a convolutional neural network (CNN).</jats:p> </jats:sec> <jats:sec> <jats:title>Methods and results</jats:title> <jats:p>After pre-training a CNN (VGG-16) on a large data set (ImageNet data set; 14 million images belonging to 1000 classes), the convolutional part was employed to transform Doppler tracings to 1D arrays. Among 366 eligible patients [age: 79.8 ± 6.77 years; 146 (39.9%) women] with pre-procedural echocardiography and right heart catheterization prior to transcatheter aortic valve replacement (TAVR), good quality Doppler tracings from 101 patients were analysed. The convolutional part of the pre-trained VGG-16 model in conjunction with principal component analysis and k-means clustering distinguished two shapes of aortic outflow velocity profiles. Kaplan–Meier analysis revealed that mortality in patients from Cluster 2 (n = 40, 39.6%) was significantly increased [hazard ratio (HR) for 2-year mortality: 3; 95% confidence interval (CI): 1–8.9]. Apart from reduced cardiac output and mean aortic valve gradient, patients from Cluster 2 were also characterized by signs of pulmonary hypertension, impaired right ventricular function, and right atrial enlargement. After training an extreme gradient boosting algorithm on these 101 patients, validation on the remaining 265 patients confirmed that patients assigned to Cluster 2 show increased mortality (HR for 2-year mortality: 2.6;
Thanaj M, Mielke J, McGurk K, et al., 2022, Genetic and environmental determinants of diastolic heart function, Nature Cardiovascular Research, Vol: 1, Pages: 361-371, ISSN: 2731-0590
Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends onmyocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processesand is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiacmotion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wideassociation study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomericfunction under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes wereindependent predictors of diastolic function and we found a causal relationship between genetically-determined ventricularstiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolicfunction that are relevant for identifying causal relationships and potential tractable targets.
Gale-Grant O, Fenn-Moltu S, França LGS, et al., 2022, Effects of gestational age at birth on perinatal structural brain development in healthy term-born babies, Human Brain Mapping, Vol: 43, Pages: 1577-1589, ISSN: 1065-9471
Infants born in early term (37-38 weeks gestation) experience slower neurodevelopment than those born at full term (40-41 weeks gestation). While this could be due to higher perinatal morbidity, gestational age at birth may also have a direct effect on the brain. Here we characterise brain volume and white matter correlates of gestational age at birth in healthy term-born neonates and their relationship to later neurodevelopmental outcome using T2 and diffusion weighted MRI acquired in the neonatal period from a cohort (n = 454) of healthy babies born at term age (>37 weeks gestation) and scanned between 1 and 41 days after birth. Images were analysed using tensor-based morphometry and tract-based spatial statistics. Neurodevelopment was assessed at age 18 months using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III). Infants born earlier had higher relative ventricular volume and lower relative brain volume in the deep grey matter, cerebellum and brainstem. Earlier birth was also associated with lower fractional anisotropy, higher mean, axial, and radial diffusivity in major white matter tracts. Gestational age at birth was positively associated with all Bayley-III subscales at age 18 months. Regression models predicting outcome from gestational age at birth were significantly improved after adding neuroimaging features associated with gestational age at birth. This work adds to the body of evidence of the impact of early term birth and highlights the importance of considering the effect of gestational age at birth in future neuroimaging studies including term-born babies.
Fenchel D, Dimitrova R, Robinson EC, et al., 2022, Neonatal multi-modal cortical profiles predict 18-month developmental outcomes, DEVELOPMENTAL COGNITIVE NEUROSCIENCE, Vol: 54, ISSN: 1878-9293
Davies RH, Augusto JB, Bhuva A, et al., 2022, Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 24, ISSN: 1097-6647
Falkai P, Koutsouleris N, Bertsch K, et al., 2022, Concept of the Munich/Augsburg Consortium Precision in Mental Health for the German Center of Mental Health, Frontiers in Psychiatry, Vol: 13
The Federal Ministry of Education and Research (BMBF) issued a call for a new nationwide research network on mental disorders, the German Center of Mental Health (DZPG). The Munich/Augsburg consortium was selected to participate as one of six partner sites with its concept “Precision in Mental Health (PriMe): Understanding, predicting, and preventing chronicity.” PriMe bundles interdisciplinary research from the Ludwig-Maximilians-University (LMU), Technical University of Munich (TUM), University of Augsburg (UniA), Helmholtz Center Munich (HMGU), and Max Planck Institute of Psychiatry (MPIP) and has a focus on schizophrenia (SZ), bipolar disorder (BPD), and major depressive disorder (MDD). PriMe takes a longitudinal perspective on these three disorders from the at-risk stage to the first-episode, relapsing, and chronic stages. These disorders pose a major health burden because in up to 50% of patients they cause untreatable residual symptoms, which lead to early social and vocational disability, comorbidities, and excess mortality. PriMe aims at reducing mortality on different levels, e.g., reducing death by psychiatric and somatic comorbidities, and will approach this goal by addressing interdisciplinary and cross-sector approaches across the lifespan. PriMe aims to add a precision medicine framework to the DZPG that will propel deeper understanding, more accurate prediction, and personalized prevention to prevent disease chronicity and mortality across mental illnesses. This framework is structured along the translational chain and will be used by PriMe to innovate the preventive and therapeutic management of SZ, BPD, and MDD from rural to urban areas and from patients in early disease stages to patients with long-term disease courses. Research will build on platforms that include one on model systems, one on the identification and validation of predictive markers, one on the development of novel multimodal treatments, one on the regulation and streng
Meng Q, Bai W, Liu T, et al., 2022, MulViMotion: shape-aware 3D myocardial motion tracking from multi-view cardiac MRI, IEEE Transactions on Medical Imaging, ISSN: 0278-0062
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods.
Meng Q, Bai W, Liu T, et al., 2022, Multiview Motion Estimation for 3D cardiac motion tracking
Code for paper ''MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI''
Ouyang C, Biffi C, Chen C, et al., 2022, Self-supervised learning for few-shot medical image segmentation, IEEE Transactions on Medical Imaging, ISSN: 0278-0062
Fully-supervised deep learning segmentation models are inflexible when encountering new unseen semantic classes and their fine-tuning often requires significant amounts of annotated data. Few-shot semantic segmentation (FSS) aims to solve this inflexibility by learning to segment an arbitrary unseen semantically meaningful class by referring to only a few labeled examples, without involving fine-tuning. State-of-the-art FSS methods are typically designed for segmenting natural images and rely on abundant annotated data of training classes to learn image representations that generalize well to unseen testing classes. However, such a training mechanism is impractical in annotation-scarce medical imaging scenarios. To address this challenge, in this work, we propose a novel self-supervised FSS framework for medical images, named SSL-ALPNet, in order to bypass the requirement for annotations during training. The proposed method exploits superpixel-based pseudo-labels to provide supervision signals. In addition, we propose a simple yet effective adaptive local prototype pooling module which is plugged into the prototype networks to further boost segmentation accuracy. We demonstrate the general applicability of the proposed approach using three different tasks: organ segmentation of abdominal CT and MRI images respectively, and cardiac segmentation of MRI images. The proposed method yields higher Dice scores than conventional FSS methods which require manual annotations for training in our experiments.
Chen Y, Schönlieb C-B, Liò P, et al., 2022, AI-based reconstruction for fast MRI – a systematic review and meta-analysis, Proceedings of the Institute of Electrical and Electronics Engineers (IEEE), Vol: 110, Pages: 224-245, ISSN: 0018-9219
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence, deep neural networks and CS algorithms are being integrated to redefine the state of the art of fast MRI. The past several years have witnessed substantial growth in the complexity, diversity, and performance of deep learning-based CS techniques that are dedicated to fastMRI. In this meta-analysis, we systematically review the deep learning-based CS techniques for fast MRI, describe key model designs, highlight breakthroughs, and discuss promising directions. We have also introduced a comprehensive analysis framework and a classification system to assess the pivotal role of deep learning in CS-based accelerationfor MRI.
Galimberti S, Graziano F, Maas AIR, et al., 2022, Effect of frailty on 6-month outcome after traumatic brain injury: a multicentre cohort study with external validation., Lancet Neurol, Vol: 21, Pages: 153-162
BACKGROUND: Frailty is known to be associated with poorer outcomes in individuals admitted to hospital for medical conditions requiring intensive care. However, little evidence is available for the effect of frailty on patients' outcomes after traumatic brain injury. Many frailty indices have been validated for clinical practice and show good performance to predict clinical outcomes. However, each is specific to a particular clinical context. We aimed to develop a frailty index to predict 6-month outcomes in patients after a traumatic brain injury. METHODS: A cumulative deficit approach was used to create a novel frailty index based on 30 items dealing with disease states, current medications, and laboratory values derived from data available from CENTER-TBI, a prospective, longitudinal observational study of patients with traumatic brain injury presenting within 24 h of injury and admitted to a ward or an intensive care unit at 65 centres in Europe between Dec 19, 2014, and Dec 17, 2017. From the individual cumulative CENTER-TBI frailty index (range 0-30), we obtained a standardised value (range 0-1), with high scores indicating higher levels of frailty. The effect of frailty on 6-month outcome evaluated with the extended Glasgow Outcome Scale (GOSE) was assessed through a proportional odds logistic model adjusted for known outcome predictors. An unfavourable outcome was defined as death or severe disability (GOSE score ≤4). External validation was performed on data from TRACK-TBI, a prospective observational study co-designed with CENTER-TBI, which enrolled patients with traumatic brain injury at 18 level I trauma centres in the USA from Feb 26, 2014, to July 27, 2018. CENTER-TBI is registered with ClinicalTrials.gov, NCT02210221; TRACK-TBI is registered at ClinicalTrials.gov, NCT02119182. FINDINGS: 2993 participants (median age was 51 years [IQR 30-67], 2058 [69%] were men) were included in this analysis. The overall median CENTER-TBI frailty index score was 0
Hinterwimmer F, Lazic I, Suren C, et al., 2022, Machine learning in knee arthroplasty: specific data are key-a systematic review., Knee Surg Sports Traumatol Arthrosc, Vol: 30, Pages: 376-388
PURPOSE: Artificial intelligence (AI) in healthcare is rapidly growing and offers novel options of data analysis. Machine learning (ML) represents a distinct application of AI, which is capable of generating predictions and has already been tested in different medical specialties with various approaches such as diagnostic applications, cost predictions or identification of risk factors. In orthopaedics, this technology has only recently been introduced and the literature on ML in knee arthroplasty is scarce. In this review, we aim to investigate which predictions are already feasible using ML models in knee arthroplasty to identify prerequisites for the effective use of this novel approach. For this reason, we conducted a systematic review of ML algorithms for outcome prediction in knee arthroplasty. METHODS: A comprehensive search of PubMed, Medline database and the Cochrane Library was conducted to find ML applications for knee arthroplasty. All relevant articles were systematically retrieved and evaluated by an orthopaedic surgeon and a data scientist on the basis of the PRISMA statement. The search strategy yielded 225 articles of which 19 were finally assessed as eligible. A modified Coleman Methodology Score (mCMS) was applied to account for a methodological evaluation. RESULTS: The studies presented in this review demonstrated fair to good results (AUC median 0.76/range 0.57-0.98), while heterogeneous prediction models were analysed: complications (6), costs (4), functional outcome (3), revision (2), postoperative satisfaction (2), surgical technique (1) and biomechanical properties (1) were investigated. The median mCMS was 65 (range 40-80) points. CONCLUSION: The prediction of distinct outcomes with ML models applying specific data is already feasible; however, the prediction of more complex outcomes is still inaccurate. Registry data on knee arthroplasty have not been fully analysed yet so that specific parameters have not been sufficiently evaluated. The
Liu T, Meng Q, Huang J-J, et al., 2022, Video summarization through reinforcement learning with a 3D spatio-temporal U-Net, IEEE Transactions on Image Processing, Vol: 31, Pages: 1573-1586, ISSN: 1057-7149
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames. In this paper, we introduce the 3DST-UNet-RL framework for video summarization. A 3D spatio-temporal U-Net is used to efficiently encode spatio-temporal information of the input videos for downstream reinforcement learning (RL). An RL agent learns from spatio-temporal latent scores and predicts actions for keeping or rejecting a video frame in a video summary. We investigate if real/inflated 3D spatio-temporal CNN features are better suited to learn representations from videos than commonly used 2D image features. Our framework can operate in both, a fully unsupervised mode and a supervised training mode. We analyse the impact of prescribed summary lengths and show experimental evidence for the effectiveness of 3DST-UNet-RL on two commonly used general video summarization benchmarks. We also applied our method on a medical video summarization task. The proposed video summarization method has the potential to save storage costs of ultrasound screening videos as well as to increase efficiency when browsing patient video data during retrospective analysis or audit without loosing essential information.
Nasirigerdeh R, Torkzadehmahani R, Matschinske J, et al., 2022, sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies, GENOME BIOLOGY, Vol: 23, ISSN: 1474-760X
Jia X, Thorley A, Chen W, et al., 2022, Learning a Model-Driven Variational Network for Deformable Image Registration, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 41, Pages: 199-212, ISSN: 0278-0062
Hinterwimmer F, Lazic I, Langer S, et al., 2022, Prediction of complications and surgery duration in primary TKA with high accuracy using machine learning with arthroplasty-specific data, Knee Surgery, Sports Traumatology, Arthroscopy, ISSN: 0942-2056
Purpose: The number of primary total knee arthroplasties (TKA) is expected to rise constantly. For patients and healthcare providers, the early identification of risk factors therefore becomes increasingly fundamental in the context of precision medicine. Others have already investigated the detection of risk factors by conducting literature reviews and applying conventional statistical methods. Since the prediction of events has been moderately accurate, a more comprehensive approach is needed. Machine learning (ML) algorithms have had ample success in many disciplines. However, these methods have not yet had a significant impact in orthopaedic research. The selection of a data source as well as the inclusion of relevant parameters is of utmost importance in this context. In this study, a standardized approach for ML in TKA to predict complications during surgery and an irregular surgery duration using data from two German arthroplasty-specific registries was evaluated. Methods: The dataset is based on two initiatives of the German Society for Orthopaedics and Orthopaedic Surgery. A problem statement and initial parameters were defined. After screening, cleaning and preparation of these datasets, 864 cases of primary TKA (2016–2019) were gathered. The XGBoost algorithm was chosen and applied with a hyperparameter search, a cross validation and a loss weighting to cope with class imbalance. For final evaluation, several metrics (accuracy, sensitivity, specificity, AUC) were calculated. Results: An accuracy of 92.0%, sensitivity of 34.8%, specificity of 95.8%, and AUC of 78.0% were achieved for predicting complications in primary TKA and 93.4%, 74.0%, 96.3%, and 91.6% for predicting irregular surgery duration, respectively. While traditional statistics (correlation coefficient) could not find any relevant correlation between any two parameters, the feature importance revealed several non-linear outcomes. Conclusion: In this study, a feasible ML model to predict
Meissen F, Kaissis G, Rueckert D, 2022, AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation, 24th Int Conf on Med Image Comp and Comp Assisted Intervent (MICCAI) / Conf on Mitosis Domain Generalizat Challenge (MIDOG) / Conf on Med Out-of-Distribut Analysis Challenge (MOOD) / Conf on Learn2Reg (L2R), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 127-135, ISSN: 0302-9743
Sideri-Lampretsa V, Kaissis G, Rueckert D, 2022, Multi-Modal Unsupervised Brain Image Registration Using Edge Maps, ISSN: 1945-7928
Diffeomorphic deformable multi-modal image registration is a challenging task which aims to bring images acquired by different modalities to the same coordinate space and at the same time to preserve the topology and the invertibility of the transformation. Recent research has focused on leveraging deep learning approaches for this task as these have been shown to achieve competitive registration accuracy while being computationally more efficient than traditional iterative registration methods. In this work, we propose a simple yet effective unsupervised deep learning-based multi-modal image registration approach that benefits from auxiliary information coming from the gradient magnitude of the image, i.e. the image edges, during the training. The intuition behind this is that image locations with a strong gradient are assumed to denote a transition of tissues, which are locations of high information value able to act as a geometry constraint. The task is similar to using segmentation maps to drive the training, but the edge maps are easier and faster to acquire and do not require annotations. We evaluate our approach in the context of registering multi-modal (T1w to T2w) magnetic resonance (MR) brain images of different subjects using three different loss functions that are said to assist multi-modal registration, showing that in all cases the auxiliary information leads to better results without compromising the runtime.
Zolotareva O, Nasirigerdeh R, Matschinske J, et al., 2021, Flimma: a federated and privacy-aware tool for differential gene expression analysis, Genome Biology, Vol: 22, ISSN: 1474-7596
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.
Qin C, Duan J, Hammernik K, et al., 2021, Complementary time-frequency domain networks for dynamic parallel MR image reconstruction, MAGNETIC RESONANCE IN MEDICINE, Vol: 86, Pages: 3274-3291, ISSN: 0740-3194
Dimitrova R, Pietsch M, Ciarrusta J, et al., 2021, Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age, NeuroImage, Vol: 243, ISSN: 1053-8119
INTRODUCTION: The dynamic nature and complexity of the cellular events that take place during the last trimester of pregnancy make the developing cortex particularly vulnerable to perturbations. Abrupt interruption to normal gestation can lead to significant deviations to many of these processes, resulting in atypical trajectory of cortical maturation in preterm birth survivors. METHODS: We sought to first map typical cortical micro and macrostructure development using invivo MRI in a large sample of healthy term-born infants scanned after birth (n=259). Then we offer a comprehensive characterisation of the cortical consequences of preterm birth in 76 preterm infants scanned at term-equivalent age (37-44 weeks postmenstrual age). We describe the group-average atypicality, the heterogeneity across individual preterm infants, and relate individual deviations from normative development to age at birth and neurodevelopment at 18 months. RESULTS: In the term-born neonatal brain, we observed heterogeneous and regionally specific associations between age at scan and measures of cortical morphology and microstructure, including rapid surface expansion, greater cortical thickness, lower cortical anisotropy and higher neurite orientation dispersion. By term-equivalent age, preterm infants had on average increased cortical tissue water content and reduced neurite density index in the posterior parts of the cortex, and greater cortical thickness anteriorly compared to term-born infants. While individual preterm infants were more likely to show extreme deviations (over 3.1 standard deviations) from normative cortical maturation compared to term-born infants, these extreme deviations were highly variable and showed very little spatial overlap between individuals. Measures of regional cortical development were associated with age at birth, but not with neurodevelopment at 18 months. CONCLUSION: We showed that preterm birth alters cortical micro and macrostructural maturation near
Eyre M, Fitzgibbon SP, Ciarrusta J, et al., 2021, The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity (vol 144, awab118, 2021), BRAIN, Vol: 144, ISSN: 0006-8950
Matthew J, Skelton E, Day TG, et al., 2021, Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time, Prenatal Diagnosis, Vol: 42, Pages: 49-59, ISSN: 0197-3851
ObjectiveAdvances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end-to-end automation of the mid-trimester screening ultrasound scan using AI-enabled tools.MethodsA prospective method comparison study was conducted. Participants had both standard and AI-assisted US scans performed. The AI tools automated image acquisition, biometric measurement, and report production. A feedback survey captured the sonographers' perceptions of scanning.ResultsTwenty-three subjects were studied. The average time saving per scan was 7.62 min (34.7%) with the AI-assisted method (p < 0.0001). There was no difference in reporting time. There were no clinically significant differences in biometric measurements between the two methods. The AI tools saved a satisfactory view in 93% of the cases (four core views only), and 73% for the full 13 views, compared to 98% for both using the manual scan. Survey responses suggest that the AI tools helped sonographers to concentrate on image interpretation by removing disruptive tasks.ConclusionSeparating freehand scanning from image capture and measurement resulted in a faster scan and altered workflow. Removing repetitive tasks may allow more attention to be directed identifying fetal malformation. Further work is required to improve the image plane detection algorithm for use in real time.
De Marvao A, McGurk K, Zheng S, et al., 2021, Outcomes and phenotypic expression of rare variants in hypertrophic cardiomyopathy genes in over 200,000 adults, ESC Congress 2021, Publisher: European Society of Cardiology, Pages: 1731-1731, ISSN: 0195-668X
BackgroundHypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomere-encoding genes, but little is known about the clinical significance of these variants in the general population.PurposeTo determine the population prevalence of HCM-associated sarcomeric variants, characterise their phenotypic manifestations, estimate penetrance, and identify associations between sarcomeric variants and clinical outcomes, we performed an observational study of 218,813 adults in the UK Biobank (UKBB), of whom 200,584 have whole exome sequencing (WES).MethodsWe carried out an integrated analysis of WES and cardiac magnetic resonance (CMR) imaging in UK Biobank participants stratified by sarcomere-encoding variant status. Computer vision techniques were used to automatically segment the four chambers of the heart (Figure 1). Cardiac motion analysis was used to derive strain and strain rates. Regional analysis of left ventricular wall thickness was performed using three-dimensional modelling of these segmentations.ResultsMedian age at recruitment was 58 (IQR 50–63 years), and participants were followed up for a median of 10.8 years (IQR 9.9–11.6 years) with a total of 19,507 primary clinical events reported.The prevalence of rare variants (allele frequency <0.ehab724.17314) in HCM-associated sarcomere-encoding genes in 200,584 participants was 2.9% (n=5,727; 1 in 35), and the prevalence of pathogenic or likely pathogenic variants (SARC-P/LP) was 0.24% (n=474, 1 in 423).SARC-P/LP variants were associated with increased risk of death or major adverse cardiac events (MACE) compared to controls (HR 1.68, 95% CI 1.37–2.06, p<0.001), mainly due to heart failure endpoints (Figure 2: cumulative hazard curves with zoomed plots for lifetime risk of A) death and MACE or B) heart failure, stratified by genotype; genotype negative (SARC-NEG), carriers of indeterminate sarcomeric variants (SARC-IND) or SARC-P/LP; C) Forest plot of comparative lifetime risk of c
Wang S, Qin C, Savioli N, et al., 2021, Joint motion correction and super resolution for cardiac segmentationvia latent optimisation, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the limit of acquisition duration andrespiratory/cardiac motion, stacks of multi-slice 2D images are acquired inclinical routine. The segmentation of these images provides a low-resolution representation of cardiac anatomy, which may contain artefacts caused by motion. Here we propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations. Given a low-resolution segmentation as input, the framework accounts for inter-slice motion in cardiac MR imaging and super-resolves the input into a high-resolution segmentation consistent with input. A multi-view loss is incorporated to leverage information from both short-axis view and long-axis view of cardiac imaging. To solve the inverse problem, iterative optimisation is performed in a latent space, which ensures the anatomical plausibility. This alleviates the need of paired low-resolution and high-resolution images for supervised learning. Experiments on two cardiac MR datasets show that the proposed framework achieves high performance, comparable to state-of-the-art super-resolution approaches and with better cross-domain generalisability and anatomical plausibility.
Makowski MR, Bressem KK, Franz L, et al., 2021, De Novo Radiomics Approach Using Image Augmentation and Features From T1 Mapping to Predict Gleason Scores in Prostate Cancer., Invest Radiol, Vol: 56, Pages: 661-668
OBJECTIVES: The aims of this study were to discriminate among prostate cancers (PCa's) with Gleason scores 6, 7, and ≥8 on biparametric magnetic resonance imaging (bpMRI) of the prostate using radiomics and to evaluate the added value of image augmentation and quantitative T1 mapping. MATERIALS AND METHODS: Eighty-five patients with subsequently histologically proven PCa underwent bpMRI at 3 T (T2-weighted imaging, diffusion-weighted imaging) with 66 patients undergoing additional T1 mapping at 3 T. The PCa lesions as well as the peripheral and transition zones were segmented pixel by pixel in multiple slices of the 3D MRI data sets (T2-weighted images, apparent diffusion coefficient, and T1 maps). To increase the size of the data set, images were augmented for contrast, brightness, noise, and perspective multiple times, effectively increasing the sample size 10-fold, and 322 different radiomics features were extracted before and after augmentation. Four different machine learning algorithms, including a random forest (RF), stochastic gradient boosting (SGB), support vector machine (SVM), and k-nearest neighbor, were trained with and without features from T1 maps to differentiate among 3 different Gleason groups (6, 7, and ≥8). RESULTS: Support vector machine showed the highest accuracy of 0.92 (95% confidence interval [CI], 0.62-1.00) for classifying the different Gleason scores, followed by RF (0.83; 95% CI, 0.52-0.98), SGB (0.75; 95% CI, 0.43-0.95), and k-nearest neighbor (0.50; 95% CI, 0.21-0.79). Image augmentation resulted in an average increase in accuracy between 0.08 (SGB) and 0.48 (SVM). Removing T1 mapping features led to a decline in accuracy for RF (-0.16) and SGB (-0.25) and a higher generalization error. CONCLUSIONS: When data are limited, image augmentations and features from quantitative T1 mapping sequences might help to achieve higher accuracy and lower generalization error for classification among different Gleason groups in bpMRI by using
Chen C, Hammernik K, Ouyang C, et al., 2021, Cooperative training and latent space data augmentation for robust medical image segmentation, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
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