Publications
926 results found
Sutton J, Menten MJ, Riedl S, et al., 2023, Correction: Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration-the PINNACLE trial protocol, Eye, Vol: 37, Pages: 1-1, ISSN: 0950-222X
Fenn-Moltu S, Fitzgibbon SP, Ciarrusta J, et al., 2023, Development of neonatal brain functional centrality and alterations associated with preterm birth, Cerebral Cortex, Vol: 33, Pages: 5585-5596, 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.
Sutton J, Menten MJ, Riedl S, et al., 2023, Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration-the PINNACLE trial protocol, Eye, Vol: 37, Pages: 1275-1283, ISSN: 0950-222X
AimsAge-related macular degeneration (AMD) is characterised by a progressive loss of central vision. Intermediate AMD is a risk factor for progression to advanced stages categorised as geographic atrophy (GA) and neovascular AMD. However, rates of progression to advanced stages vary between individuals. Recent advances in imaging and computing technologies have enabled deep phenotyping of intermediate AMD. The aim of this project is to utilise machine learning (ML) and advanced statistical modelling as an innovative approach to discover novel features and accurately quantify markers of pathological retinal ageing that can individualise progression to advanced AMD.MethodsThe PINNACLE study consists of both retrospective and prospective parts. In the retrospective part, more than 400,000 optical coherent tomography (OCT) images collected from four University Teaching Hospitals and the UK Biobank Population Study are being pooled, centrally stored and pre-processed. With this large dataset featuring eyes with AMD at various stages and healthy controls, we aim to identify imaging biomarkers for disease progression for intermediate AMD via supervised and unsupervised ML. The prospective study part will firstly characterise the progression of intermediate AMD in patients followed between one and three years; secondly, it will validate the utility of biomarkers identified in the retrospective cohort as predictors of progression towards late AMD. Patients aged 55–90 years old with intermediate AMD in at least one eye will be recruited across multiple sites in UK, Austria and Switzerland for visual function tests, multimodal retinal imaging and genotyping. Imaging will be repeated every four months to identify early focal signs of deterioration on spectral-domain optical coherence tomography (OCT) by human graders. A focal event triggers more frequent follow-up with visual function and imaging tests. The primary outcome is the sensitivity and specificity of the OCT ima
Hinterwimmer F, Lazic I, Langer S, et al., 2023, Prediction of complications and surgery duration in primary TKA with high accuracy using machine learning with arthroplasty-specific data., Knee Surg Sports Traumatol Arthrosc, Vol: 31, Pages: 1323-1333
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 outco
Cruz G, Hammernik K, Kuestner T, et al., 2023, Single-heartbeat cardiac cine imaging via jointly regularized non-rigid motion corrected reconstruction, NMR in Biomedicine, Pages: 1-33, ISSN: 0952-3480
PURPOSE: Develop a novel approach for 2D breath-hold cardiac cine from a single heartbeat, by combining cardiac motion corrected reconstructions and non-rigidly aligned patch-based regularization. METHODS: Conventional cardiac cine imaging is obtained via motion resolved reconstructions of data acquired over multiple heartbeats. Here, we achieve single-heartbeat cine imaging by incorporating non-rigid cardiac motion correction into the reconstruction of each cardiac phase, in conjunction with a motion-aligned patch-based regularization. The proposed Motion Corrected CINE (MC-CINE) incorporates all acquired data into the reconstruction of each (motion corrected) cardiac phase, resulting in a better posed problem than motion resolved approaches. MC-CINE was compared to iterative SENSE and XD-GRASP in fourteen healthy subjects in terms of image sharpness, reader scoring (1-5 range) and reader ranking (1-9 range) of image quality, and single-slice left ventricular assessment. RESULTS: MC-CINE was significantly superior to both iterative SENSE and XD-GRASP using 20, 2 and 1 heartbeat(s). Iterative SENSE, XD-GRASP and MC-CINE achieved sharpness of 74%, 74% and 82% using 20 heartbeats, and 53%, 66% and 82% with 1 heartbeat, respectively. Corresponding results for reader scores were 4.0, 4.7 and 4.9, with 20 heartbeats, and 1.1, 3.0 and 3.9 with 1 heartbeat. Corresponding results for reader rankings were 5.3, 7.3 and 8.6 with 20 heartbeats, and 1.0, 3.2 and 5.4 with 1 heartbeat. MC-CINE using a single heartbeat presented non-significant differences in image quality to iterative SENSE with 20 heartbeats. MC-CINE and XD-GRASP at one heartbeat both presented a non-significant negative bias of <2% in ejection fraction relative to the reference iterative SENSE. CONCLUSION: The proposed MC-CINE significantly improves image quality relative to iterative SENSE and XD-GRASP, enabling 2D cine from a single heartbeat.
Williams LZJ, Fitzgibbon SP, Bozek J, et al., 2023, Structural and functional asymmetry of the neonatal cerebral cortex, NATURE HUMAN BEHAVIOUR, ISSN: 2397-3374
Hinterwimmer F, Consalvo S, Wilhelm N, et al., 2023, SAM-X: sorting algorithm for musculoskeletal x-ray radiography., Eur Radiol, Vol: 33, Pages: 1537-1544
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
Menten MJ, Holland R, Leingang O, et al., 2023, Exploring healthy retinal aging with deep learning, Ophthalmology Science, Vol: 3, Pages: 1-10, ISSN: 2666-9145
PurposeTo study the individual course of retinal changes caused by healthy aging using deep learning.DesignRetrospective analysis of a large data set of retinal OCT images.ParticipantsA total of 85 709 adults between the age of 40 and 75 years of whom OCT images were acquired in the scope of the UK Biobank population study.MethodsWe created a counterfactual generative adversarial network (GAN), a type of neural network that learns from cross-sectional, retrospective data. It then synthesizes high-resolution counterfactual OCT images and longitudinal time series. These counterfactuals allow visualization and analysis of hypothetical scenarios in which certain characteristics of the imaged subject, such as age or sex, are altered, whereas other attributes, crucially the subject’s identity and image acquisition settings, remain fixed.Main Outcome MeasuresUsing our counterfactual GAN, we investigated subject-specific changes in the retinal layer structure as a function of age and sex. In particular, we measured changes in the retinal nerve fiber layer (RNFL), combined ganglion cell layer plus inner plexiform layer (GCIPL), inner nuclear layer to the inner boundary of the retinal pigment epithelium (INL-RPE), and retinal pigment epithelium (RPE).ResultsOur counterfactual GAN is able to smoothly visualize the individual course of retinal aging. Across all counterfactual images, the RNFL, GCIPL, INL-RPE, and RPE changed by −0.1 μm ± 0.1 μm, −0.5 μm ± 0.2 μm, −0.2 μm ± 0.1 μm, and 0.1 μm ± 0.1 μm, respectively, per decade of age. These results agree well with previous studies based on the same cohort from the UK Biobank population study. Beyond population-wide average measures, our counterfactual GAN allows us to explore whether the retinal layers of a given eye will increase in thickness, decrease in thickness, or stagnate as a subject ages.ConclusionThis study demonstrates how counterfactual GANs
Woodrow RE, Winzeck S, Luppi AI, et al., 2023, Acute thalamic connectivity precedes chronic post-concussive symptoms in mild traumatic brain injury., Brain
Chronic postconcussive symptoms are common after mild traumatic brain injury (mTBI), and are difficult to predict or treat. Thalamic functional integrity is particularly vulnerable in mTBI, and may be related to long-term outcomes, but requires further investigation. We compared structural magnetic resonance imaging (MRI) and resting state functional MRI in 108 patients with a Glasgow Coma Scale (GCS) of 13 to 15 and normal CT, and 76 controls. We examined whether acute changes in thalamic functional connectivity were early markers for persistent symptoms, and explored neurochemical associations of our findings using data from positron emission tomography. Of the mTBI cohort, 47% showed incomplete recovery 6 months post-injury. Despite the absence of structural changes, we found acute thalamic hyperconnectivity in mTBI, with specific vulnerabilities of individual thalamic nuclei. Acute fMRI markers differentiated those with chronic postconcussive symptoms, with time- and outcome-dependent relationships in a sub-cohort followed longitudinally. Moreover, emotional and cognitive symptoms were associated with changes in thalamic functional connectivity to known dopaminergic and noradrenergic targets, respectively. Our findings suggest that chronic symptoms can have a basis in early thalamic pathophysiology. This may aid identification of patients at risk of chronic postconcussive symptoms following mTBI, provide a basis for development of new therapies, and could facilitate precision medicine application of these therapies.
Ma Q, Li L, Robinson EC, et al., 2023, CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 42, Pages: 430-443, ISSN: 0278-0062
Hammernik K, Kustner T, Yaman B, et al., 2023, Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging, IEEE Signal Processing Magazine, Vol: 40, Pages: 98-114, ISSN: 1053-5888
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments in incorporating physics information into learning-based MRI reconstruction. We consider inverse problems with both linear and nonlinear forward models for computational MRI and review the classical approaches for solving these. We then focus on physics-driven deep learning approaches, covering physics-driven loss functions, plug-and-play (PnP) methods, generative models, and unrolled networks. We highlight domain-specific challenges, such as real- and complex-valued building blocks of neural networks, and translational applications in MRI with linear and nonlinear forward models. Finally, we discuss common issues and open challenges, and we draw connections to the importance of physics-driven learning when combined with other downstream tasks in the medical imaging pipeline.
Qin C, Wang S, Chen C, et al., 2023, Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior, MEDICAL IMAGE ANALYSIS, Vol: 83, ISSN: 1361-8415
Zimmer VA, Gomez A, Skelton E, et 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.
Gatidis S, Kart T, Fischer M, et al., 2022, Better together: data harmonization and cross-study analysis of abdominal MRI data from UK biobank and the German national cohort., Investigative Radiology, Vol: 58, Pages: 346-354, ISSN: 0020-9996
OBJECTIVES: The UK Biobank (UKBB) and German National Cohort (NAKO) are among the largest cohort studies, capturing a wide range of health-related data from the general population, including comprehensive magnetic resonance imaging (MRI) examinations. The purpose of this study was to demonstrate how MRI data from these large-scale studies can be jointly analyzed and to derive comprehensive quantitative image-based phenotypes across the general adult population. MATERIALS AND METHODS: Image-derived features of abdominal organs (volumes of liver, spleen, kidneys, and pancreas; volumes of kidney hilum adipose tissue; and fat fractions of liver and pancreas) were extracted from T1-weighted Dixon MRI data of 17,996 participants of UKBB and NAKO based on quality-controlled deep learning generated organ segmentations. To enable valid cross-study analysis, we first analyzed the data generating process using methods of causal discovery. We subsequently harmonized data from UKBB and NAKO using the ComBat approach for batch effect correction. We finally performed quantile regression on harmonized data across studies providing quantitative models for the variation of image-derived features stratified for sex and dependent on age, height, and weight. RESULTS: Data from 8791 UKBB participants (49.9% female; age, 63 ± 7.5 years) and 9205 NAKO participants (49.1% female, age: 51.8 ± 11.4 years) were analyzed. Analysis of the data generating process revealed direct effects of age, sex, height, weight, and the data source (UKBB vs NAKO) on image-derived features. Correction of data source-related effects resulted in markedly improved alignment of image-derived features between UKBB and NAKO. Cross-study analysis on harmonized data revealed comprehensive quantitative models for the phenotypic variation of abdominal organs across the general adult population. CONCLUSIONS: Cross-study analysis of MRI data from UKBB and NAKO as proposed in this work can be helpful for futur
Neubauer A, Menegaux A, Wendt J, et 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.
Richter S, Winzeck S, Czeiter E, et al., 2022, Serum biomarkers identify critically ill traumatic brain injury patients for MRI, CRITICAL CARE, Vol: 26, ISSN: 1364-8535
Ouyang C, Chen C, Li S, et al., 2022, Causality-inspired single-source domain generalization for medical image segmentation, IEEE Transactions on Medical Imaging, Vol: 42, Pages: 1095-1106, 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.
Kart T, Fischer M, Winzeck S, et 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
Chen C, Qin C, Ouyang C, et 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.
Hinterwimmer F, Consalvo S, Neumann J, et 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
Guo Y, Al-Jibury E, Garcia-Millan R, et 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.
Chen C, Li Z, Ouyang C, et 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.
Consalvo S, Hinterwimmer F, Neumann J, et 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
Bercea C, Wiestler B, Rueckert D, et al., 2022, Federated disentangled representation learning for unsupervised brain anomaly detection, NATURE MACHINE INTELLIGENCE, Vol: 4, Pages: 685-+
Hinterwimmer F, Consalvo S, Neumann J, et 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.
Bloier M, Hinterwimmer F, Breden S, et 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.
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, 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.
van der Vlegel M, Mikolić A, Lee Hee Q, et 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.
Kaissis G, Knolle M, Jungmann F, et 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.
Åkerlund CAI, Holst A, Stocchetti N, et 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
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