Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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914 results found

Zimmer VA, Gomez A, Skelton E, Wright R, Wheeler G, Deng S, Ghavami N, Lloyd K, Matthew J, Kainz B, Rueckert D, Hajnal JV, Schnabel JAet al., 2023, Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view, Medical Image Analysis, Vol: 83, ISSN: 1361-8415

Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the model can learn from larger and more diverse datasets while improving the accuracy of the segmentation task in particular in limited training set conditions. With this approach we investigate the variability in annotations from multiple raters and show that our automatic segmentations (Dice of 0.86 for anterior and 0.83 for posterior placentas) achieve human-level performance as compared to intra- and inter-observer variability. Lastly, our approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation. This results in high quality segmentation of larger structures such as the placenta in US with reduced image artifacts which are beyond the field-of-view of single probes.

Journal article

Qin C, Wang S, Chen C, Bai W, Rueckert Det al., 2023, Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior, MEDICAL IMAGE ANALYSIS, Vol: 83, ISSN: 1361-8415

Journal article

Neubauer A, Menegaux A, Wendt J, Li HB, Schmitz-Koep B, Ruzok T, Thalhammer M, Schinz D, Bartmann P, Wolke D, Priller J, Zimmer C, Rueckert D, Hedderich DM, Sorg Cet al., 2022, Aberrant claustrum structure in preterm-born neonates: an MRI study., NeuroImage: Clinical, Vol: 37, Pages: 1-16, ISSN: 2213-1582

The human claustrum is a gray matter structure in the white matter between insula and striatum. Previous analysis found altered claustrum microstructure in very preterm-born adults associated with lower cognitive performance. As the claustrum development is related to hypoxia-ischemia sensitive transient cell populations being at-risk in premature birth, we hypothesized that claustrum structure is already altered in preterm-born neonates. We studied anatomical and diffusion-weighted MRIs of 83 preterm- and 83 term-born neonates at term-equivalent age. Additionally, claustrum development was analyzed both in a spectrum of 377 term-born neonates and longitudinally in 53 preterm-born subjects. Data was provided by the developing Human Connectome Project. Claustrum development showed increasing volume, increasing fractional anisotropy (FA), and decreasing mean diffusivity (MD) around term both across term- and preterm-born neonates. Relative to term-born ones, preterm-born neonates had (i) increased absolute and relative claustrum volumes, both indicating increased cellular and/or extracellular matter and being in contrast to other subcortical gray matter regions of decreased volumes such as thalamus; (ii) lower claustrum FA and higher claustrum MD, pointing at increased extracellular matrix and impaired axonal integrity; and (iii) aberrant covariance between claustrum FA and MD, respectively, and that of distributed gray matter regions, hinting at relatively altered claustrum microstructure. Results together demonstrate specifically aberrant claustrum structure in preterm-born neonates, suggesting altered claustrum development in prematurity, potentially relevant for later cognitive performance.

Journal article

Richter S, Winzeck S, Czeiter E, Amrein K, Kornaropoulos EN, Verheyden J, Sugar G, Yang Z, Wang K, Maas AIR, Steyerberg E, Büki A, Newcombe VFJ, Menon DK, Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury Magnetic Resonance Imaging CENTER-TBI MRI Sub-study Participants and Investigatorset al., 2022, Serum biomarkers identify critically ill traumatic brain injury patients for MRI., Crit Care, Vol: 26

BACKGROUND: Magnetic resonance imaging (MRI) carries prognostic importance after traumatic brain injury (TBI), especially when computed tomography (CT) fails to fully explain the level of unconsciousness. However, in critically ill patients, the risk of deterioration during transfer needs to be balanced against the benefit of detecting prognostically relevant information on MRI. We therefore aimed to assess if day of injury serum protein biomarkers could identify critically ill TBI patients in whom the risks of transfer are compensated by the likelihood of detecting management-altering neuroimaging findings. METHODS: Data were obtained from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Eligibility criteria included: TBI patients aged ≥ 16 years, Glasgow Coma Score (GCS) < 13 or patient intubated with unrecorded pre-intubation GCS, CT with Marshall score < 3, serum biomarkers (GFAP, NFL, NSE, S100B, Tau, UCH-L1) sampled ≤ 24 h of injury, MRI < 30 days of injury. The degree of axonal injury on MRI was graded using the Adams-Gentry classification. The association between serum concentrations of biomarkers and Adams-Gentry stage was assessed and the optimum threshold concentration identified, assuming different minimum sensitivities for the detection of brainstem injury (Adams-Gentry stage 3). A cost-benefit analysis for the USA and UK health care settings was also performed. RESULTS: Among 65 included patients (30 moderate-severe, 35 unrecorded) axonal injury was detected in 54 (83%) and brainstem involvement in 33 (51%). In patients with moderate-severe TBI, brainstem injury was associated with higher concentrations of NSE, Tau, UCH-L1 and GFAP. If the clinician did not want to miss any brainstem injury, NSE could have avoided MRI transfers in up to 20% of patients. If a 94% sensitivity was accepted considering potential transfer

Journal article

Ouyang C, Chen C, Li S, Li Z, Qin C, Bai W, Rueckert Det al., 2022, Causality-inspired single-source domain generalization for medical image segmentation, IEEE Transactions on Medical Imaging, Pages: 1-1, ISSN: 0278-0062

Deep learning models usually suffer from the domain shift issue, where models trained on one source domain do not generalize well to other unseen domains. In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data are only available from one source domain, which is common in medical imaging applications. We tackle this problem in the context of cross-domain medical image segmentation. In this scenario, domain shifts are mainly caused by different acquisition processes. We propose a simple causality-inspired data augmentation approach to expose a segmentation model to synthesized domain-shifted training examples. Specifically, 1) to make the deep model robust to discrepancies in image intensities and textures, we employ a family of randomly-weighted shallow networks. They augment training images using diverse appearance transformations. 2) Further we show that spurious correlations among objects in an image are detrimental to domain robustness. These correlations might be taken by the network as domain-specific clues for making predictions, and they may break on unseen domains. We remove these spurious correlations via causal intervention. This is achieved by resampling the appearances of potentially correlated objects independently. The proposed approach is validated on three cross-domain segmentation scenarios: cross-modality (CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI segmentation, and cross-site prostate MRI segmentation. The proposed approach yields consistent performance gains compared with competitive methods when tested on unseen domains.

Journal article

Fenn-Moltu S, Fitzgibbon SP, Ciarrusta J, Eyre M, Cordero-Grande L, Chew A, Falconer S, Gale-Grant O, Harper N, Dimitrova R, Vecchiato K, Fenchel D, Javed A, Earl M, Price AN, Hughes E, Duff EP, O'Muircheartaigh J, Nosarti C, Arichi T, Rueckert D, Counsell S, Hajnal J, Edwards AD, McAlonan G, Batalle Det al., 2022, Development of neonatal brain functional centrality and alterations associated with preterm birth, Cerebral Cortex, Pages: 1-12, ISSN: 1047-3211

Formation of the functional connectome in early life underpins future learning and behavior. However, our understanding of how the functional organization of brain regions into interconnected hubs (centrality) matures in the early postnatal period is limited, especially in response to factors associated with adverse neurodevelopmental outcomes such as preterm birth. We characterized voxel-wise functional centrality (weighted degree) in 366 neonates from the Developing Human Connectome Project. We tested the hypothesis that functional centrality matures with age at scan in term-born babies and is disrupted by preterm birth. Finally, we asked whether neonatal functional centrality predicts general neurodevelopmental outcomes at 18 months. We report an age-related increase in functional centrality predominantly within visual regions and a decrease within the motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age had higher functional centrality predominantly within visual regions and lower measures in motor regions. Functional centrality was not related to outcome at 18 months old. Thus, preterm birth appears to affect functional centrality in regions undergoing substantial development during the perinatal period. Our work raises the question of whether these alterations are adaptive or disruptive and whether they predict neurodevelopmental characteristics that are more subtle or emerge later in life.

Journal article

Kart T, Fischer M, Winzeck S, Glocker B, Bai W, Buelow R, Emmel C, Friedrich L, Kauczor H-U, Keil T, Kroencke T, Mayer P, Niendorf T, Peters A, Pischon T, Schaarschmidt BM, Schmidt B, Schulze MB, Umutle L, Voelzke H, Kuestner T, Bamberg F, Schoelkopf B, Rueckert D, Gatidis Set al., 2022, Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies, SCIENTIFIC REPORTS, Vol: 12, ISSN: 2045-2322

Journal article

Chen C, Qin C, Ouyang C, Li Z, Wang S, Qiu H, Chen L, Tarroni G, Bai W, Rueckert Det al., 2022, Enhancing MR image segmentation with realistic adversarial data augmentation, Medical Image Analysis, Vol: 82, Pages: 1-15, ISSN: 1361-8415

The success of neural networks on medical image segmentation tasks typicallyrelies on large labeled datasets for model training. However, acquiring andmanually labeling a large medical image set is resource-intensive, expensive,and sometimes impractical due to data sharing and privacy issues. To addressthis challenge, we propose AdvChain, a generic adversarial data augmentationframework, aiming at improving both the diversity and effectiveness of trainingdata for medical image segmentation tasks. AdvChain augments data with dynamicdata augmentation, generating randomly chained photo-metric and geometrictransformations to resemble realistic yet challenging imaging variations toexpand training data. By jointly optimizing the data augmentation model and asegmentation network during training, challenging examples are generated toenhance network generalizability for the downstream task. The proposedadversarial data augmentation does not rely on generative networks and can beused as a plug-in module in general segmentation networks. It iscomputationally efficient and applicable for both low-shot supervised andsemi-supervised learning. We analyze and evaluate the method on two MR imagesegmentation tasks: cardiac segmentation and prostate segmentation with limitedlabeled data. Results show that the proposed approach can alleviate the needfor labeled data while improving model generalization ability, indicating itspractical value in medical imaging applications.

Journal article

Hinterwimmer F, Consalvo S, Wilhelm N, Seidl F, Burgkart RHH, von Eisenhart-Rothe R, Rueckert D, Neumann Jet al., 2022, SAM-X: sorting algorithm for musculoskeletal x-ray radiography., Eur Radiol

OBJECTIVE: To develop a two-phased deep learning sorting algorithm for post-X-ray image acquisition in order to facilitate large musculoskeletal image datasets according to their anatomical entity. METHODS: In total, 42,608 unstructured and pseudonymized radiographs were retrieved from the PACS of a musculoskeletal tumor center. In phase 1, imaging data were sorted into 1000 clusters by a self-supervised model. A human-in-the-loop radiologist assigned weak, semantic labels to all clusters and clusters with the same label were merged. Three hundred thirty-two non-musculoskeletal clusters were discarded. In phase 2, the initial model was modified by "injecting" the identified labels into the self-supervised model to train a classifier. To provide statistical significance, data split and cross-validation were applied. The hold-out test set consisted of 50% external data. To gain insight into the model's predictions, Grad-CAMs were calculated. RESULTS: The self-supervised clustering resulted in a high normalized mutual information of 0.930. The expert radiologist identified 28 musculoskeletal clusters. The modified model achieved a classification accuracy of 96.2% and 96.6% for validation and hold-out test data for predicting the top class, respectively. When considering the top two predicted class labels, an accuracy of 99.7% and 99.6% was accomplished. Grad-CAMs as well as final cluster results underlined the robustness of the proposed method by showing that it focused on similar image regions a human would have considered for categorizing images. CONCLUSION: For efficient dataset building, we propose an accurate deep learning sorting algorithm for classifying radiographs according to their anatomical entity in the assessment of musculoskeletal diseases. KEY POINTS: • Classification of large radiograph datasets according to their anatomical entity. • Paramount importance of structuring vast amounts of retrospective data for modern deep learning app

Journal article

Zhuang X, Xu J, Luo X, Chen C, Ouyang C, Rueckert D, Campello VM, Lekadir K, Vesal S, RaviKumar N, Liu Y, Luo G, Chen J, Li H, Ly B, Sermesant M, Roth H, Zhu W, Wang J, Ding X, Wang X, Yang S, Li Let al., 2022, Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge, MEDICAL IMAGE ANALYSIS, Vol: 81, ISSN: 1361-8415

Journal article

Hinterwimmer F, Consalvo S, Neumann J, Rueckert D, von Eisenhart-Rothe R, Burgkart Ret al., 2022, Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies-a scoping review., Eur Radiol, Vol: 32, Pages: 7173-7184

Musculoskeletal malignancies are a rare type of cancer. Consequently, sufficient imaging data for machine learning (ML) applications is difficult to obtain. The main purpose of this review was to investigate whether ML is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies and what the respective reasons for this might be. A scoping review was conducted by a radiologist, an orthopaedic surgeon and a data scientist to identify suitable articles based on the PRISMA statement. Studies meeting the following criteria were included: primary malignant musculoskeletal tumours, machine/deep learning application, imaging data or data retrieved from images, human/preclinical, English language and original research. Initially, 480 articles were found and 38 met the eligibility criteria. Several continuous and discrete parameters related to publication, patient distribution, tumour specificities, ML methods, data and metrics were extracted from the final articles. For the synthesis, diagnosis-oriented studies were further examined by retrieving the number of patients and labels and metric scores. No significant correlations between metrics and mean number of samples were found. Several studies presented that ML could support imaging-driven diagnosis of musculoskeletal malignancies in distinct cases. However, data quality and quantity must be increased to achieve clinically relevant results. Compared to the experience of an expert radiologist, the studies used small datasets and mostly included only one type of data. Key to critical advancement of ML models for rare diseases such as musculoskeletal malignancies is a systematic, structured data collection and the establishment of (inter)national networks to obtain substantial datasets in the future. KEY POINTS: • Machine learning does not yet significantly impact imaging-driven diagnosis for musculoskeletal malignancies compared to other disciplines such as lung, breast or CNS cancer. • R

Journal article

Guo Y, Al-Jibury E, Garcia-Millan R, Ntagiantas K, King JWD, Nash AJ, Galjart N, Lenhard B, Rueckert D, Fisher AG, Pruessner G, Merkenschlager Met al., 2022, Chromatin jets define the properties of cohesin-driven in vivo loop extrusion, Molecular Cell, Vol: 82, Pages: 3769-3780.e5, ISSN: 1097-2765

Complex genomes show intricate organization in three-dimensional (3D) nuclear space. Current models posit that cohesin extrudes loops to form self-interacting domains delimited by the DNA binding protein CTCF. Here, we describe and quantitatively characterize cohesin-propelled, jet-like chromatin contacts as landmarks of loop extrusion in quiescent mammalian lymphocytes. Experimental observations and polymer simulations indicate that narrow origins of loop extrusion favor jet formation. Unless constrained by CTCF, jets propagate symmetrically for 1-2 Mb, providing an estimate for the range of in vivo loop extrusion. Asymmetric CTCF binding deflects the angle of jet propagation as experimental evidence that cohesin-mediated loop extrusion can switch from bi- to unidirectional and is controlled independently in both directions. These data offer new insights into the physiological behavior of in vivo cohesin-mediated loop extrusion and further our understanding of the principles that underlie genome organization.

Journal article

Chen C, Li Z, Ouyang C, Sinclair M, Bai W, Rueckert Det al., 2022, MaxStyle: adversarial style composition for robust medical image segmentation, Medical Image Computing and Computer Assisted Interventions (MICCAI) 2022

Convolutional neural networks (CNNs) have achieved remarkable segmentationaccuracy on benchmark datasets where training and test sets are from the samedomain, yet their performance can degrade significantly on unseen domains,which hinders the deployment of CNNs in many clinical scenarios. Most existingworks improve model out-of-domain (OOD) robustness by collecting multi-domaindatasets for training, which is expensive and may not always be feasible due toprivacy and logistical issues. In this work, we focus on improving modelrobustness using a single-domain dataset only. We propose a novel dataaugmentation framework called MaxStyle, which maximizes the effectiveness ofstyle augmentation for model OOD performance. It attaches an auxiliarystyle-augmented image decoder to a segmentation network for robust featurelearning and data augmentation. Importantly, MaxStyle augments data withimproved image style diversity and hardness, by expanding the style space withnoise and searching for the worst-case style composition of latent features viaadversarial training. With extensive experiments on multiple public cardiac andprostate MR datasets, we demonstrate that MaxStyle leads to significantlyimproved out-of-distribution robustness against unseen corruptions as well ascommon distribution shifts across multiple, different, unseen sites and unknownimage sequences under both low- and high-training data settings. The code canbe found at https://github.com/cherise215/MaxStyle.

Conference paper

Ma Q, Li L, Robinson EC, Kainz B, Rueckert D, Alansary Aet al., 2022, CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs., IEEE Trans Med Imaging, Vol: PP

We present CortexODE, a deep learning framework for cortical surface reconstruction. CortexODE leverages neural ordinary differential equations (ODEs) to deform an input surface into a target shape by learning a diffeomorphic flow. The trajectories of the points on the surface are modeled as ODEs, where the derivatives of their coordinates are parameterized via a learnable Lipschitz-continuous deformation network. This provides theoretical guarantees for the prevention of self-intersections. CortexODE can be integrated to an automatic learning-based pipeline, which reconstructs cortical surfaces efficiently in less than 5 seconds. The pipeline utilizes a 3D U-Net to predict a white matter segmentation from brain Magnetic Resonance Imaging (MRI) scans, and further generates a signed distance function that represents an initial surface. Fast topology correction is introduced to guarantee homeomorphism to a sphere. Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively. The proposed pipeline is evaluated on large-scale neuroimage datasets in various age groups including neonates (25-45 weeks), young adults (22-36 years) and elderly subjects (55-90 years). Our experiments demonstrate that the CortexODE-based pipeline can achieve less than 0.2mm average geometric error while being orders of magnitude faster compared to conventional processing pipelines.

Journal article

Consalvo S, Hinterwimmer F, Neumann J, Steinborn M, Salzmann M, Seidl F, Lenze U, Knebel C, Rueckert D, Burgkart RHHet al., 2022, Two-Phase Deep Learning Algorithm for Detection and Differentiation of Ewing Sarcoma and Acute Osteomyelitis in Paediatric Radiographs., Anticancer Res, Vol: 42, Pages: 4371-4380

BACKGROUND/AIM: Ewing sarcoma is a highly malignant tumour predominantly found in children. The radiological signs of this malignancy can be mistaken for acute osteomyelitis. These entities require profoundly different treatments and result in completely different prognoses. The purpose of this study was to develop an artificial intelligence algorithm, which can determine imaging features in a common radiograph to distinguish osteomyelitis from Ewing sarcoma. MATERIALS AND METHODS: A total of 182 radiographs from our Sarcoma Centre (118 healthy, 44 Ewing, 20 osteomyelitis) from 58 different paediatric (≤18 years) patients were collected. All localisations were taken into consideration. Cases of acute, acute on chronic osteomyelitis and intraosseous Ewing sarcoma were included. Chronic osteomyelitis, extra-skeletal Ewing sarcoma, malignant small cell tumour and soft tissue-based primitive neuroectodermal tumours were excluded. The algorithm development was split into two phases and two different classifiers were built and combined with a Transfer Learning approach to cope with the very limited amount of data. In phase 1, pathological findings were differentiated from healthy findings. In phase 2, osteomyelitis was distinguished from Ewing sarcoma. Data augmentation and median frequency balancing were implemented. A data split of 70%, 15%, 15% for training, validation and hold-out testing was applied, respectively. RESULTS: The algorithm achieved an accuracy of 94.4% on validation and 90.6% on test data in phase 1. In phase 2, an accuracy of 90.3% on validation and 86.7% on test data was achieved. Grad-CAM results revealed regions, which were significant for the algorithms decision making. CONCLUSION: Our AI algorithm can become a valuable support for any physician involved in treating musculoskeletal lesions to support the diagnostic process of detection and differentiation of osteomyelitis from Ewing sarcoma. Through a Transfer Learning approach, the algorithm wa

Journal article

Bercea C, Wiestler B, Rueckert D, Albarqouni Set al., 2022, Federated disentangled representation learning for unsupervised brain anomaly detection, NATURE MACHINE INTELLIGENCE, Vol: 4, Pages: 685-+

Journal article

Taoudi-Benchekroun Y, Christiaens D, Grigorescu I, Gale-Grant O, Schuh A, Pietsch M, Chew A, Harper N, Falconer S, Poppe T, Hughes E, Hutter J, Price AN, Tournier J-D, Cordero-Grande L, Counsell SJ, Rueckert D, Arichi T, Hajnal J, Edwards AD, Deprez M, Batalle Det al., 2022, Predicting age and clinical risk from the neonatal connectome, NEUROIMAGE, Vol: 257, ISSN: 1053-8119

Journal article

Sutton J, Menten MJ, Riedl S, Bogunovic H, Leingang O, Anders P, Hagag AM, Waldstein S, Wilson A, Cree AJ, Traber G, Fritsche LG, Scholl H, Rueckert D, Schmidt-Erfurth U, Sivaprasad S, Prevost T, Lotery Aet al., 2022, Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration-the PINNACLE trial protocol (May, 10.1038/s41433-022-02097-0, 2022), EYE, ISSN: 0950-222X

Journal article

Meng Q, Bai W, Liu T, Simoes Monteiro de Marvao A, O'Regan D, Rueckert Det al., 2022, MulViMotion: shape-aware 3D myocardial motion tracking from multi-view cardiac MRI, IEEE Transactions on Medical Imaging, Vol: 41, Pages: 1961-1974, 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.

Journal article

Hinterwimmer F, Consalvo S, Neumann J, Micheler C, Wilhelm N, Lang J, von Eisenhart-Rothe R, Burgkart R, Rueckert Det al., 2022, From Self-supervised Learning to Transfer Learning with Musculoskeletal Radiographs, Current Directions in Biomedical Engineering, Vol: 8, Pages: 9-12

Ewing sarcomas are malignant neoplasm entities typically found in children and adolescents. Early detection is crucial for therapy and prognosis. Due to the low incidence the general experience as well as according data is limited. Novel support tools for diagnosis, such as deep learning models for image interpretation, are required. While acquiring sufficient data is a common obstacle in medicine, several techniques to tackle small data sets have emerged. The general necessity of large data sets in addition to a rare disease lead to the question whether transfer learning can solve the issue of limited data and subsequently support tasks such as distinguishing Ewing sarcoma from its main differential diagnosis (acute osteomyelitis) in paediatric radiographs. 42,608 unstructured radiographs from our musculoskeletal tumour centre were retrieved from the PACS. The images were clustered with a DeepCluster, a self-supervised algorithm. 1000 clusters were used for the upstream task (pretraining). Following, the pretrained classification network was applied for the downstream task of differentiating Ewing sarcoma and acute osteomyelitis. An untrained network achieved an accuracy of 81.5%/54.2%, while an ImageNet-pretrained network resulted in 89.6%/70.8% for validation and testing, respectively. Our transfer learning approach surpassed the best result by 4.4%/17.3% percentage points. Transfer learning demonstrated to be a powerful technique to support image interpretation tasks. Even for small data sets, the impact can be significant. However, transfer learning is not a final solution to small data sets. To achieve clinically relevant results, a structured and systematic data acquisition is of paramount importance.

Journal article

Bloier M, Hinterwimmer F, Breden S, Consalvo S, Neumann J, Wilhelm N, von Eisenhart-Rothe R, Rueckert D, Burgkart Ret al., 2022, Detection and Segmentation of Heterogeneous Bone Tumours in Limited Radiographs, Current Directions in Biomedical Engineering, Vol: 8, Pages: 69-72

Bone tumours are a rare and often highly malignant entity. Early clinical diagnosis is the most important step, but the difficulty of detecting and assessing bone malignancies is in its radiological peculiarity and limited experience of non-experts. Since X-ray imaging is the first imaging method of bone tumour diagnostics, the purpose of this study is to develop an artificial intelligence (AI) model to detect and segment the tumorous tissue in a radiograph. We investigated which methods are necessary to cope with limited and heterogeneous data. We collected 531 anonymised radiographs from our musculoskeletal tumour centre. In order to adapt to the complexity of recognizing the malignant tissue and cope with limited data, transfer learning, data augmentation as well as several architectures, some of which were initially designed for medical images, were implemented. Furthermore, dataset size was varied by adding another bone tumour entity. We applied a data split of 72%, 18%, 10% for training, validation and testing, respectively. To provide statistical significance and robustness, we applied a cross-validation and image stratification with respect to tumour pixels present. We achieved an accuracy of 99.72% and an intersection over union of 87.43% for hold-out test data by applying several methods to tackle limited data. Transfer learning and additional data brought the greatest performance increase. In conclusion, our model was able to detect and segment tumorous tissue in radiographs with good performance, although it was trained on a very limited amount of data. Transfer Learning and data augmentation proved to significantly mitigate the issue of limited data samples. However, to accomplish clinical significance, more data has to be acquired in the future. Through minor adjustments, the model could be adapted to other musculoskeletal tumour entities and become a general support tool for orthopaedic surgeons and radiologists.

Journal article

van der Vlegel M, Mikolić A, Lee Hee Q, Kaplan ZLR, Retel Helmrich IRA, van Veen E, Andelic N, Steinbuechel NV, Plass AM, Zeldovich M, Wilson L, Maas AIR, Haagsma JA, Polinder S, CENTER-TBI Participants and Investigatorset al., 2022, Health care utilization and outcomes in older adults after Traumatic Brain Injury: A CENTER-TBI study., Injury, Vol: 53, Pages: 2774-2782

INTRODUCTION: The incidence of Traumatic Brain Injury (TBI) is increasingly common in older adults aged ≥65 years, forming a growing public health problem. However, older adults are underrepresented in TBI research. Therefore, we aimed to provide an overview of health-care utilization, and of six-month outcomes after TBI and their determinants in older adults who sustained a TBI. METHODS: We used data from the prospective multi-center Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. In-hospital and post-hospital health care utilization and outcomes were described for patients aged ≥65 years. Ordinal and linear regression analyses were performed to identify determinants of the Glasgow Outcome Scale Extended (GOSE), health-related quality of life (HRQoL), and mental health symptoms six-months post-injury. RESULTS: Of 1254 older patients, 45% were admitted to an ICU with a mean length of stay of 9 days. Nearly 30% of the patients received inpatient rehabilitation. In total, 554/1254 older patients completed the six-month follow-up questionnaires. The mortality rate was 9% after mild and 60% after moderate/severe TBI, and full recovery based on GOSE was reported for 44% of patients after mild and 6% after moderate/severe TBI. Higher age and increased injury severity were primarily associated with functional impairment, while pre-injury systemic disease, psychiatric conditions and lower educational level were associated with functional impairment, lower generic and disease-specific HRQoL and mental health symptoms. CONCLUSION: The rate of impairment and disability following TBI in older adults is substantial, and poorer outcomes across domains are associated with worse preinjury health. Nonetheless, a considerable number of patients fully or partially returns to their preinjury functioning. There should not be pessimism about outcomes in older adults who survive.

Journal article

Kaissis G, Knolle M, Jungmann F, Ziller A, Usynin D, Rueckert Det al., 2022, A UNIFIED INTERPRETATION OF THE GAUSSIAN MECHANISM FOR DIFFERENTIAL PRIVACY THROUGH THE SENSITIVITY INDEX, Journal of Privacy and Confidentiality, Vol: 12

The Gaussian mechanism (GM) represents a universally employed tool for achieving differential privacy (DP), and a large body of work has been devoted to its analysis. We argue that the three prevailing interpretations of the GM, namely (ε, δ)-DP, f-DP and Rényi DP can be expressed by using a single parameter ψ, which we term the sensitivity index. ψ uniquely characterises the GM and its properties by encapsulating its two fundamental quantities: the sensitivity of the query and the magnitude of the noise perturbation. With strong links to the ROC curve and the hypothesis-testing interpretation of DP, ψ offers the practitioner a powerful method for interpreting, comparing and communicating the privacy guarantees of Gaussian mechanisms.

Journal article

Åkerlund CAI, Holst A, Stocchetti N, Steyerberg EW, Menon DK, Ercole A, Nelson DW, CENTER-TBI Participants and Investigatorset al., 2022, Clustering identifies endotypes of traumatic brain injury in an intensive care cohort: a CENTER-TBI study., Crit Care, Vol: 26

BACKGROUND: While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as 'mild', 'moderate' or 'severe' based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights. METHODS: We developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (< 24 h) demographic, clinical, physiological, laboratory and imaging data to identify subgroups of TBI patients admitted to the intensive care unit in the CENTER-TBI dataset (N = 1,728). A cluster similarity index was used for robust determination of optimal cluster number. Mutual information was used to quantify feature importance and for cluster interpretation. RESULTS: Six stable endotypes were identified with distinct GCS and composite systemic metabolic stress profiles, distinguished by GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, a cluster with 'moderate' TBI (by traditional classification) and deranged metabolic profile, had a worse outcome than a cluster with 'severe' GCS and a normal metabolic profile. Addition of cluster labels significantly improved the prognostic precision of the IMPACT (International Mission for Prognosis and Analysis of Clinical trials in TBI) extended model, for prediction of both unfavourable outcome and mortality (both p < 0.001). CONCLUSIONS: Six stable and clinically distinct TBI endotypes were identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates

Journal article

Lachmann M, Rippen E, Rueckert D, Schuster T, Xhepa E, von Scheidt M, Pellegrini C, Trenkwalder T, Rheude T, Stundl A, Thalmann R, Harmsen G, Yuasa S, Schunkert H, Kastrati A, Joner M, Kupatt C, Laugwitz KLet 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, Vol: 3, Pages: 153-168

<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;

Journal article

Ciarrusta J, Christiaens D, Fitzgibbon SP, Dimitrova R, Hutter J, Hughes E, Duff E, Price AN, Cordero-Grande L, Tournier J-D, Rueckert D, V Hajnal J, Arichi T, McAlonan G, Edwards D, Batalle Det al., 2022, The developing brain structural and functional connectome fingerprint, Developmental Cognitive Neuroscience, Vol: 55, ISSN: 1878-9293

In the mature brain, structural and functional ‘fingerprints’ of brain connectivity can be used to identify the uniqueness of an individual. However, whether the characteristics that make a given brain distinguishable from others already exist at birth remains unknown. Here, we used neuroimaging data from the developing Human Connectome Project (dHCP) of preterm born neonates who were scanned twice during the perinatal period to assess the developing brain fingerprint. We found that 62% of the participants could be identified based on the congruence of the later structural connectome to the initial connectivity matrix derived from the earlier timepoint. In contrast, similarity between functional connectomes of the same subject at different time points was low. Only 10% of the participants showed greater self-similarity in comparison to self-to-other-similarity for the functional connectome. These results suggest that structural connectivity is more stable in early life and can represent a potential connectome fingerprint of the individual: a relatively stable structural connectome appears to support a changing functional connectome at a time when neonates must rapidly acquire new skills to adapt to their new environment.

Journal article

Holland R, Menten MJ, Leingang O, Bogunovic H, Hagag AM, Kaye R, Riedl S, Traber G, Fritsche L, Prevost T, Scholl HP, Schmidt-Erfurth U, Sivaprasad S, Rueckert D, Lotery AJet al., 2022, Self-supervised pretraining enables deep learning-based classification of AMD with fewer annotations, Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404

Conference paper

Menten MJ, Leingang O, Bogunovic H, Holland R, Hagag AM, Kaye R, Riedl S, Traber G, Fritsche L, Prevost T, Scholl HP, Schmidt-Erfurth U, Sivaprasad S, Rueckert D, Lotery AJet al., 2022, Discovery of imaging biomarkers for healthy aging and age-related macular degeneration using counterfactual generative adversarial networks, Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404

Conference paper

Leingang O, Bogunovic H, Riedl S, Chakravarty A, Menten MJ, Holland R, Traber GL, Fritsche L, Prevost T, Scholl HP, Rueckert D, Sivaprasad S, Lotery AJ, Schmidt-Erfurth Uet al., 2022, Automated deep learning-based AMD stage detection in real-world OCT datasets, Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404

Conference paper

Anders P, Traber GL, Hagag AM, Riedl S, Fritsche L, Rueckert D, Schmidt-Erfurth U, Sivaprasad S, Lotery AJ, Scholl HPet al., 2022, Investigation of intermediate AMD with MAIA microperimetry metrics by the PINNACLE Consortium, Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404

Conference paper

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