Publications
1015 results found
Zou C, Mueller A, Wolfgang U, et al., 2022, Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label, IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE, Vol: 10, ISSN: 2168-2372
Menten MJ, Paetzold JC, Dima A, et al., 2022, Physiology-Based Simulation of the Retinal Vasculature Enables Annotation-Free Segmentation of OCT Angiographs, MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VIII, Vol: 13438, Pages: 330-340, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 2
Tanzer M, Ferreira P, Scott A, et al., 2022, Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction, MEDICAL IMAGE UNDERSTANDING AND ANALYSIS, MIUA 2022, Vol: 13413, Pages: 101-115, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 1
Tanzer M, Yook SH, Ferreira P, et al., 2022, Review of Data Types and Model Dimensionality for Cardiac DTI SMS-Related Artefact Removal, STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART: REGULAR AND CMRXMOTION CHALLENGE PAPERS, STACOM 2022, Vol: 13593, Pages: 123-132, ISSN: 0302-9743
Kuestner T, Pan J, Gilliam C, et al., 2022, Self-Supervised Motion-Corrected Image Reconstruction Network for 4D Magnetic Resonance Imaging of the Body Trunk, APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING, Vol: 11, ISSN: 2048-7703
- Author Web Link
- Cite
- Citations: 1
Sideri-Lampretsa V, Kaissis G, Rueckert D, 2022, MULTI-MODAL UNSUPERVISED BRAIN IMAGE REGISTRATION USING EDGE MAPS, 19th IEEE International Symposium on Biomedical Imaging (IEEE ISBI), Publisher: IEEE, ISSN: 1945-7928
- Author Web Link
- Cite
- Citations: 1
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
Mueller P, Kaissis G, Zou C, et al., 2022, Joint Learning of Localized Representations from Medical Images and Reports, COMPUTER VISION, ECCV 2022, PT XXVI, Vol: 13686, Pages: 685-701, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 1
Qiu H, Hammernik K, Qin C, et al., 2022, Embedding Gradient-Based Optimization in Image Registration Networks, MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VI, Vol: 13436, Pages: 56-65, ISSN: 0302-9743
Muffoletto M, Xu H, Barbaroux H, et al., 2022, Comparison of Semi- and Un-Supervised Domain Adaptation Methods for Whole-Heart Segmentation, 13th International Workshop on Statistical Atlases and Computational Modelling of the Heart (STACOM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 91-100, ISSN: 0302-9743
Li L, Ma Q, Li Z, et al., 2022, Fetal Cortex Segmentation with Topology and Thickness Loss Constraints, 1st Workshop on Ethical and Philosop Issues in Med Imaging (EPIMI) / 12th Int Workshop on Multimodal Learning and Fus Across Scales for Clin Decis Support (ML-CDS) / 2nd Int Workshop on Topol Data Anal for Biomed Imaging (TDA4BiomedicalImaging), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 123-133, ISSN: 0302-9743
Meng Q, Bai W, Liu T, et al., 2022, Mesh-Based 3D Motion Tracking in Cardiac MRI Using Deep Learning, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 248-258, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 1
Mueller P, Kaissis G, Zou C, et al., 2022, Radiological Reports Improve Pre-training for Localized Imaging Tasks on Chest X-Rays, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 647-657, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 1
Qiao M, Basaran BD, Qiu H, et al., 2022, Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data, 13th International Workshop on Statistical Atlases and Computational Modelling of the Heart (STACOM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 3-12, ISSN: 0302-9743
Binzer M, Hammernik K, Rueckert D, et al., 2022, Long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-task Learning on Imaging and Tabular Data, 5th International Workshop on Predictive Intelligence in Medicine (PRIME MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 137-148, ISSN: 0302-9743
Meissen F, Kaissis G, Rueckert D, 2022, Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI, 7th International Brain Lesion Workshop (BrainLes), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 63-74, ISSN: 0302-9743
Ouyang C, Wang S, Chen C, et al., 2022, Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation, 4th International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 59-69, ISSN: 0302-9743
Pan J, Rueckert D, Kuestner T, et al., 2022, Learning-Based and Unrolled Motion-Compensated Reconstruction for Cardiac MR CINE Imaging, MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VI, Vol: 13436, Pages: 686-696, ISSN: 0302-9743
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
Henriksen P, Hammernik K, Rueckert D, et al., 2021, Bias Field Robustness Verification of Large Neural Image Classifiers, British Machine Vision Conference (BMVC21)
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
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)
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), Publisher: Springer, Pages: 14-24
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.
Hammernik K, Schlemper J, Qin C, et al., 2021, Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination, MAGNETIC RESONANCE IN MEDICINE, Vol: 86, Pages: 1859-1872, ISSN: 0740-3194
- Author Web Link
- Cite
- Citations: 24
van Veen E, van der Jagt M, Citerio G, et al., 2021, Occurrence and timing of withdrawal of life-sustaining measures in traumatic brain injury patients: a CENTER-TBI study., Intensive Care Med, Vol: 47, Pages: 1115-1129
BACKGROUND: In patients with severe brain injury, withdrawal of life-sustaining measures (WLSM) is common in intensive care units (ICU). WLSM constitutes a dilemma: instituting WLSM too early could result in death despite the possibility of an acceptable functional outcome, whereas delaying WLSM could unnecessarily burden patients, families, clinicians, and hospital resources. We aimed to describe the occurrence and timing of WLSM, and factors associated with timing of WLSM in European ICUs in patients with traumatic brain injury (TBI). METHODS: The CENTER-TBI Study is a prospective multi-center cohort study. For the current study, patients with traumatic brain injury (TBI) admitted to the ICU and aged 16 or older were included. Occurrence and timing of WLSM were documented. For the analyses, we dichotomized timing of WLSM in early (< 72 h after injury) versus later (≥ 72 h after injury) based on recent guideline recommendations. We assessed factors associated with initiating WLSM early versus later, including geographic region, center, patient, injury, and treatment characteristics with univariable and multivariable (mixed effects) logistic regression. RESULTS: A total of 2022 patients aged 16 or older were admitted to the ICU. ICU mortality was 13% (n = 267). Of these, 229 (86%) patients died after WLSM, and were included in the analyses. The occurrence of WLSM varied between regions ranging from 0% in Eastern Europe to 96% in Northern Europe. In 51% of the patients, WLSM was early. Patients in the early WLSM group had a lower maximum therapy intensity level (TIL) score than patients in the later WLSM group (median of 5 versus 10) The strongest independent variables associated with early WLSM were one unreactive pupil (odds ratio (OR) 4.0, 95% confidence interval (CI) 1.3-12.4) or two unreactive pupils (OR 5.8, CI 2.6-13.1) compared to two reactive pupils, and an Injury Severity Score (ISS) if over 41 (OR per point above
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.