695 results found
Valindria VV, Lavdas I, Bai W, et al., 2018, Domain adaptation for MRI organ segmentation using reverse classification accuracy, International Conference on Medical Imaging with Deep Learning (MIDL)
The variations in multi-center data in medical imaging studies have broughtthe necessity of domain adaptation. Despite the advancement of machine learningin automatic segmentation, performance often degrades when algorithms areapplied on new data acquired from different scanners or sequences than thetraining data. Manual annotation is costly and time consuming if it has to becarried out for every new target domain. In this work, we investigate automaticselection of suitable subjects to be annotated for supervised domain adaptationusing the concept of reverse classification accuracy (RCA). RCA predicts theperformance of a trained model on data from the new domain and differentstrategies of selecting subjects to be included in the adaptation via transferlearning are evaluated. We perform experiments on a two-center MR database forthe task of organ segmentation. We show that subject selection via RCA canreduce the burden of annotation of new data for the target domain.
Rajchl M, Pawlowski N, Rueckert D, et al., 2018, NeuroNet: fast and robust reproduction of multiple brain Image segmentation pipelines, International Conference on Medical Imaging with Deep Learning (MIDL), Publisher: MIDL
NeuroNet is a deep convolutional neural network mimicking multiple popularand state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.The network is trained on 5,000 T1-weighted brain MRI scans from the UK BiobankImaging Study that have been automatically segmented into brain tissue andcortical and sub-cortical structures using the standard neuroimaging pipelines.Training a single model from these complementary and partially overlappinglabel maps yields a new powerful "all-in-one", multi-output segmentation tool.The processing time for a single subject is reduced by an order of magnitudecompared to running each individual software package. We demonstrate very goodreproducibility of the original outputs while increasing robustness tovariations in the input data. We believe NeuroNet could be an important tool inlarge-scale population imaging studies and serve as a new standard inneuroscience by reducing the risk of introducing bias when choosing a specificsoftware package.
Koch LM, Rajchl M, Bai W, et al., 2018, Multi-atlas segmentation using partially annotated data: methods and annotation strategies, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol: 40, Pages: 1683-1696, ISSN: 0162-8828
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation.
Bruun M, Rhodius-Meester H, Baroni M, et al., 2018, Biomarkers in differential diagnosis of dementia using a data-driven approach, 4th Congress of the European-Academy-of-Neurology (EAN), Publisher: WILEY, Pages: 278-278, ISSN: 1351-5101
Cerrolaza JJ, Sinclair M, Li Y, et al., 2018, Deep learning with ultrasound physics for fetal skull segmentation, 15th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: Institute of Electrical and Electronics Engineers, Pages: 564-567, ISSN: 1945-7928
2D ultrasound (US) is still the preferred imaging method for fetal screening. However, 2D biometrics are significantly affected by the inter/intra-observer variability and operator dependence of a traditionally manual procedure. 3DUS is an alternative emerging modality with the potential to alleviate many of these problems. This paper presents a new automatic framework for skull segmentation in fetal 3DUS. We propose a two-stage convolutional neural network (CNN) able to incorporate additional contextual and structural information into the segmentation process. In the first stage of the CNN, a partial reconstruction of the skull is obtained, segmenting only those regions visible in the original US volume. From this initial segmentation, two additional channels of information are computed inspired by the underlying physics of US image acquisition: an angle incidence map and a shadow casting map. These additional information channels are combined in the second stage of the CNN to provide a complete segmentation of the skull, able to compensate for the fading and shadowing artefacts observed in the original US image. The performance of the new segmentation architecture was evaluated on a dataset of 66 cases, obtaining an average Dice coefficient of 0.83 ± 0.06. Finally, we also evaluated the clinical potential of the new 3DUS-based analysis framework for the assessment of cranial deformation, significantly outperforming traditional 2D biometrics (100% vs. 50% specificity, respectively).
Oksuz I, Ruijsink B, Puyol-Anton E, et al., 2018, Automatic left ventricular outflow tract classification for accurate cardiac MR planning, 15th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: Institute of Electrical and Electronics Engineers, Pages: 462-465, ISSN: 1945-7928
Cardiac MR planning is important to ensure high quality image data and to enable accurate quantification of cardiac function. One result of inaccurate planning is an `off-axis' orientation of the 4-chamber view, often recognized by the presence of the left ventricular outflow tract (LVOT). This can lead to difficulties in assessment of atrial volumes and septal wall motion, either manually by experts or by automated image analysis algorithms. For large datasets such as the UK biobank, manual labelling is tedious and automated analysis pipelines including automatic image quality assessment need to be developed. In this paper, we propose a method to automatically detect the presence of the LVOT in cardiac MRI, which can aid identifying poorly planned 4-chamber images. Our method is based on Convolutional Neural Networks (CNNs) and is able to detect LVOT in 4-chamber images in less than 1ms. We test our algorithm on a subset of the UK biobank dataset (246 cardiac MR images) and achieve an average accuracy of 83%. We compare our approach to a range of state of the art classification methods.
Oktay O, Schlemper J, Folgoc LL, et al., 2018, Attention U-Net: Learning Where to Look for the Pancreas., International Conference on Medical Imaging with Deep Learning (MIDL)
Schlemper J, Oktay O, Chen L, et al., 2018, Attention-Gated Networks for Improving Ultrasound Scan Plane Detection., International Conference on Medical Imaging with Deep Learning (MIDL)
Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The UNet architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution layers, pooling layers, and upsampling layers. These convolution layers learn representative features of input images and construct segmentations based on the features. However, the features learned by standard convolution layers are not distinctive when the differences among different categories are subtle in terms of intensity, location, shape, and size. In this paper, we propose a novel CNN architecture, called Dense-Res-Inception Net (DRINet), which addresses this challenging problem. The proposed DRINet consists of three blocks, namely a convolutional block with dense connections, a deconvolutional block with residual Inception modules, and an unpooling block. Our proposed architecture outperforms the U-Net in three different challenging applications, namely multi-class segmentation of cerebrospinal fluid (CSF) on brain CT images, multi-organ segmentation on abdominal CT images, multi-class brain tumour segmentation on MR images.
Valindria V, Pawlowski N, Rajchl M, et al., 2018, Multi-modal learning from unpaired images: Application to multi-organ segmentation in CT and MRI, IEEE Winter Conference on Applications of Computer Vision, Publisher: IEEE
Convolutional neural networks have been widely used in medical image segmentation. The amount of training data strongly determines the overall performance. Most approaches are applied for a single imaging modality, e.g., brain MRI. In practice, it is often difficult to acquire sufficient training data of a certain imaging modality. The same anatomical structures, however, may be visible in different modalities such as major organs on abdominal CT and MRI. In this work, we investigate the effectiveness of learning from multiple modalities to improve the segmentation accuracy on each individual modality. We study the feasibility of using a dual-stream encoder-decoder architecture to learn modality-independent, and thus, generalisable and robust features. All of our MRI and CT data are unpaired, which means they are obtained from different subjects and not registered to each other. Experiments show that multi-modal learning can improve overall accuracy over modality-specific training. Results demonstrate that information across modalities can in particular improve performance on varying structures such as the spleen.
de Marvao A, Biffi C, Walsh R, et al., 2018, Defining The Effects Of Genetic Variation Using Machine Learning Analysis Of CMRs: A Study In Hypertrophic Cardiomyopathy And In A Healthy Population, Joint Meeting of the British-Society-of-Cardiovascular-Imaging/British-Society-of-Cardiovascular-CT, British-Society-of-Cardiovascular-Magnetic-Resonance and British-Nuclear-Cardiac-Society on British Cardiovascular Imaging, Publisher: BMJ PUBLISHING GROUP, Pages: A7-A8, ISSN: 1355-6037
Qin C, Guerrero R, Bowles C, et al., 2018, A large margin algorithm for automated segmentation of white matter hyperintensity, PATTERN RECOGNITION, Vol: 77, Pages: 150-159, ISSN: 0031-3203
Oksuz I, Ruijsink B, Puyol-Anton E, et al., 2018, AUTOMATIC MIS-TRIGGERING ARTEFACT DETECTION FOR IMAGE QUALITY ASSESSMENT OF CARDIAC MRI, Joint Meeting of the British-Society-of-Cardiovascular-Imaging/British-Society-of-Cardiovascular-CT, British-Society-of-Cardiovascular-Magnetic-Resonance and British-Nuclear-Cardiac-Society on British Cardiovascular Imaging, Publisher: BMJ PUBLISHING GROUP, Pages: A5-A5, ISSN: 1355-6037
Tolonen A, Rhodius-Meester HFM, Bruun M, et al., 2018, Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier, FRONTIERS IN AGING NEUROSCIENCE, Vol: 10, ISSN: 1663-4365
Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer’s disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different
Sinclair M, Baumgartner CF, Matthew J, et al., Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks, arXiv
Measurement of head biometrics from fetal ultrasonography images is of keyimportance in monitoring the healthy development of fetuses. However, theaccurate measurement of relevant anatomical structures is subject to largeinter-observer variability in the clinic. To address this issue, an automatedmethod utilizing Fully Convolutional Networks (FCN) is proposed to determinemeasurements of fetal head circumference (HC) and biparietal diameter (BPD). AnFCN was trained on approximately 2000 2D ultrasound images of the head withannotations provided by 45 different sonographers during routine screeningexaminations to perform semantic segmentation of the head. An ellipse is fittedto the resulting segmentation contours to mimic the annotation typicallyproduced by a sonographer. The model's performance was compared withinter-observer variability, where two experts manually annotated 100 testimages. Mean absolute model-expert error was slightly better thaninter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mmvs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our resultsdemonstrate that the model performs at a level similar to a human expert, andlearns to produce accurate predictions from a large dataset annotated by manysonographers. Additionally, measurements are generated in near real-time at15fps on a GPU, which could speed up clinical workflow for both skilled andtrainee sonographers.
Dawes TJW, Serrani M, Bai W, et al., Myocardial trabeculae improve left ventricular function: a combined UK Biobank and computational analysis, GAT Annual Scientific Meeting 2018, Publisher: Association of Anaesthetists of Great Britain and Ireland
Huizinga W, Poot DHJ, Vernooij MW, et al., 2018, A spatio-temporal reference model of the aging brain, NEUROIMAGE, Vol: 169, Pages: 11-22, ISSN: 1053-8119
Rueckert D, 2018, Machine learning for image segmentation, 37th Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), Publisher: ELSEVIER IRELAND LTD, Pages: S183-S183, ISSN: 0167-8140
Garcia KE, Robinson EC, Alexopoulos D, et al., 2018, Dynamic patterns of cortical expansion during folding of the preterm human brain, Proceedings of the National Academy of Sciences, Vol: 115, Pages: 3156-3161, ISSN: 0027-8424
During the third trimester of human brain development, the cerebral cortex undergoes dramatic surface expansion and folding. Physical models suggest that relatively rapid growth of the cortical gray matter helps drive this folding, and structural data suggest that growth may vary in both space (by region on the cortical surface) and time. In this study, we propose a unique method to estimate local growth from sequential cortical reconstructions. Using anatomically constrained multimodal surface matching (aMSM), we obtain accurate, physically guided point correspondence between younger and older cortical reconstructions of the same individual. From each pair of surfaces, we calculate continuous, smooth maps of cortical expansion with unprecedented precision. By considering 30 preterm infants scanned two to four times during the period of rapid cortical expansion (28-38 wk postmenstrual age), we observe significant regional differences in growth across the cortical surface that are consistent with the emergence of new folds. Furthermore, these growth patterns shift over the course of development, with noninjured subjects following a highly consistent trajectory. This information provides a detailed picture of dynamic changes in cortical growth, connecting what is known about patterns of development at the microscopic (cellular) and macroscopic (folding) scales. Since our method provides specific growth maps for individual brains, we are also able to detect alterations due to injury. This fully automated surface analysis, based on tools freely available to the brain-mapping community, may also serve as a useful approach for future studies of abnormal growth due to genetic disorders, injury, or other environmental variables.
Bowles C, Gunn R, Hammers A, et al., 2018, Modelling the Progression of Alzheimer's Disease in MRI Using Generative Adversarial Networks, Conference on Medical Imaging - Image Processing, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Suinesiaputra A, Ablin P, Alba X, et al., 2018, Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 22, Pages: 503-515, ISSN: 2168-2194
Kamnitsas K, Bai W, Ferrante E, et al., 2018, Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation, MICCAI BrainLes Workshop
Makropoulos A, Robinson EC, Schuh A, et al., 2018, The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction., NeuroImage, Vol: 173, Pages: 88-112, ISSN: 1053-8119
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
Sinclair M, Peressutti D, Puyol-Anton E, et al., 2018, Myocardial strain computed at multiple spatial scales from tagged magnetic resonance imaging: Estimating cardiac biomarkers for CRT patients, MEDICAL IMAGE ANALYSIS, Vol: 43, Pages: 169-185, ISSN: 1361-8415
Oksuz I, Clough J, Bustin A, et al., 2018, Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstruction, 1st Workshop on Machine Learning for Medical Image Reconstruction (MLMIR) held as part of the 21st Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 21-29, ISSN: 0302-9743
Knoll F, Maier A, Rueckert D, 2018, Preface, ISBN: 9783030001285
Kuklisova-Murgasova M, Estrin GL, Nunes RG, et al., 2018, Distortion Correction in Fetal EPI Using Non-Rigid Registration With a Laplacian Constraint, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 37, Pages: 12-19, ISSN: 0278-0062
Oksuz I, Ruijsink B, Puyol-Anton E, et al., 2018, Deep Learning Using K-Space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection, 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) / 8th Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 250-258, ISSN: 0302-9743
Tarroni G, Oktay O, Sinclair M, et al., 2018, A Comprehensive Approach for Learning-Based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG
Duan J, Schlemper J, Bai W, et al., 2018, Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG
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