698 results found
Snape P, Pszczolkowski S, Zafeiriou S, et al., 2016, A robust similarity measure for volumetric image registration with outliers, Image and Vision Computing, Vol: 52, Pages: 97-113, ISSN: 0262-8856
Image registration under challenging realistic conditions is a very important area of research. In this paper, we focus on algorithms that seek to densely align two volumetric images according to a global similarity measure. Despite intensive research in this area, there is still a need for similarity measures that are robust to outliers common to many different types of images. For example, medical image data is often corrupted by intensity inhomogeneities and may contain outliers in the form of pathologies. In this paper we propose a global similarity measure that is robust to both intensity inhomogeneities and outliers without requiring prior knowledge of the type of outliers. We combine the normalised gradients of images with the cosine function and show that it is theoretically robust against a very general class of outliers. Experimentally, we verify the robustness of our measures within two distinct algorithms. Firstly, we embed our similarity measures within a proof-of-concept extension of the Lucas-Kanade algorithm for volumetric data. Finally, we embed our measures within a popular non-rigid alignment framework based on free-form deformations and show it to be robust against both simulated tumours and intensity inhomogeneities.
Rajchl M, Lee M, Oktay O, et al., 2016, DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks, Publisher: arXiv:1605.07866
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations. It extends the approach of the well-known GrabCut method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naive approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.
Parsiot S, Arslan S, Passerat-Palmbach J, et al., 2016, Group-wise Parcellation of the Cortex through Multi-scale Spectral Clustering, Neuroimage, Vol: 136, Pages: 68-83, ISSN: 1095-9572
The delineation of functionally and structurally distinct regions as well as their connectivity can provide key knowledge towards understanding the brain's behaviour and function. Cytoarchitecture has long been the gold standard for such parcellation tasks, but has poor scalability and cannot be mapped in vivo. Functional and diffusion magnetic resonance imaging allow in vivo mapping of brain's connectivity and the parcellation of the brain based on local connectivity information. Several methods have been developed for single subject connectivity driven parcellation, but very few have tackled the task of group-wise parcellation, which is essential for uncovering group specific behaviours. In this paper, we propose a group-wise connectivity-driven parcellation method based on spectral clustering that captures local connectivity information at multiple scales and directly enforces correspondences between subjects. The method is applied to diffusion Magnetic Resonance Imaging driven parcellation on two independent groups of 50 subjects from the Human Connectome Project. Promising quantitative and qualitative results in terms of information loss, modality comparisons, group consistency and inter-group similarities demonstrate the potential of the method.
Schmidt-Richberg A, Ledig C, Guerrero R, et al., 2016, Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease, PLOS One, Vol: 11, ISSN: 1932-6203
Being able to estimate a patient’s progress in the course of Alzheimer’s disease and predictingfuture progression based on a number of observed biomarker values is of great interestfor patients, clinicians and researchers alike. In this work, an approach for disease progressestimation is presented. Based on a set of subjects that convert to a more severe diseasestage during the study, models that describe typical trajectories of biomarker values in thecourse of disease are learned using quantile regression. A novel probabilistic method isthen derived to estimate the current disease progress as well as the rate of progression ofan individual by fitting acquired biomarkers to the models. A particular strength of themethod is its ability to naturally handle missing data. This means, it is applicable even if individualbiomarker measurements are missing for a subject without requiring a retraining ofthe model. The functionality of the presented method is demonstrated using synthetic and—employing cognitive scores and image-based biomarkers—real data from the ADNIstudy. Further, three possible applications for progress estimation are demonstrated tounderline the versatility of the approach: classification, construction of a spatio-temporal diseaseprogression atlas and prediction of future disease progression.
Kainz B, Lloyd D, Alansary A, et al., High-Performance Motion Correction of Fetal MRI, EuroRVVV -- EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization, Publisher: Eurographics Digital Library
Fetal Magnetic Resonance Imaging (MRI) shows promising results for pre-natal diagnostics. The detection of potentially lifethreateningabnormalities in the fetus can be difficult with ultrasound alone. MRI is one of the few safe alternative imagingmodalities in pregnancy. However, to date it has been limited by unpredictable fetal and maternal motion during acquisition.Motion between the acquisitions of individual slices of a 3D volume results in spatial inconsistencies that can be resolved byslice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms to solve this problemhave evolved from very slow implementations targeting a single organ to general high-performance solutions to reconstruct thewhole uterus. In this paper we give a brief overview over the current state-of-the art in fetal motion compensation methods andshow currently emerging clinical applications of these techniques
Bernard O, Bosch JG, Heyde B, et al., 2016, Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography, IEEE Transactions on Medical Imaging, Vol: 35, Pages: 967-977, ISSN: 1558-254X
Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.
Tong T, Gao Q, Guerrero R, et al., 2016, A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer's disease, IEEE Transactions on Biomedical Engineering, Vol: 64, Pages: 155-165, ISSN: 1558-2531
OBJECTIVE: Identifying mild cognitive impairment (MCI) subjects who will progress to Alzheimer's disease is not only crucial in clinical practice, but also has a significant potential to enrich clinical trials. The purpose of this study is to develop an effective biomarker for an accurate prediction of MCI-to-AD conversion from magnetic resonance (MR) images. METHODS: We propose a novel grading biomarker for the prediction of MCI-to-AD conversion. First, we comprehensively study the effects of several important factors on the performance in the prediction task including registration accuracy, age correction, feature selection and the selection of training data. Based on the studies of these factors, a grading biomarker is then calculated for each MCI subject using sparse representation techniques. Finally, the grading biomarker is combined with age and cognitive measures to provide a more accurate prediction of MCI-to-AD conversion. RESULTS: Using the ADNI dataset, the proposed global grading biomarker achieved an area under the receiver operating characteristic curve (AUC) in the range of 79%-81% for the prediction of MCI-to-AD conversion within 3 years in 10-fold cross validations. The classification AUC further increases to 84%-92% when age and cognitive measures are combined with the proposed grading biomarker. CONCLUSION: The obtained accuracy of the proposed biomarker benefits from the contributions of different factors: a tradeoff registration level to align images to the template space; the removal of the normal aging effect; selection of discriminative voxels; the calculation of the grading biomarker using AD and normal control groups; the integration of sparse representation technique and the combination of cognitive measures. SIGNIFICANCE: The evaluation on the ADNI dataset shows the efficacy of the proposed biomarker and demonstrates a significant contribution in accurate prediction of MCI-to-AD conversion.
Koikkalainen J, Rhodius-Meester H, Tolonen A, et al., 2016, Differential diagnosis of neurodegenerative diseases using structural MRI data, NeuroImage: Clinical, Vol: 11, Pages: 435-449, ISSN: 2213-1582
Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia.Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making.A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by util
Dawes T, de Marvao A, Shi W, et al., 2016, Use of artificial intelligence to predict survival in pulmonary hypertension, Spring Meeting on Clinician Scientists in Training, Publisher: ELSEVIER SCIENCE INC, Pages: 35-35, ISSN: 0140-6736
de Marvao A, Meyer H, Dawes T, et al., 2016, Development of integrated high-resolution three-dimensional MRI and computational modelling techniques to identify novel genetic and anthropometric determinants of cardiac form and function, Spring Meeting on Clinician Scientists in Training, Publisher: ELSEVIER SCIENCE INC, Pages: 36-36, ISSN: 0140-6736
Ferrazzi G, Nunes RG, Arichi T, et al., 2016, An exploration of task based fMRI in neonates using echo-shifting to allow acquisition at longer T-E without loss of temporal efficiency, NEUROIMAGE, Vol: 127, Pages: 298-306, ISSN: 1053-8119
Peressutti D, Bai W, Shi W, et al., 2016, Towards left ventricular scar localisation using local motion descriptors, 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015, Publisher: Springer, Pages: 30-39, ISSN: 0302-9743
We propose a novel technique for the localisation of Left Ventricular (LV) scar based on local motion descriptors. Cardiac MR imaging is employed to construct a spatio-temporal motion atlas where the LV motion of different subjects can be directly compared. Local motion descriptors are derived from the motion atlas and dictionary learning is used for scar classification. Preliminary results on a cohort of 20 patients show a sensitivity and specificity of 80% and 87% in a binary classification setting.
Bai W, Oktay O, Rueckert D, 2016, Classification of myocardial infarcted patients by combining shape and motion features, 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015, Publisher: Springer, Pages: 140-145, ISSN: 0302-9743
Myocardial infarction changes both the shape and motion of the heart. In this work, cardiac shape and motion features are extracted from shape models at ED and ES phases and combined to train a SVM classifier between myocardial infarcted cases and asymptomatic cases. Shape features are characterised by PCA coefficients of a shape model, whereas motion features include wall thickening and wall motion. Evaluated on the STACOM 2015 challenge dataset, the proposed method achieves a high accuracy of 97.5% for classification, which shows that shape and motion features can be useful biomarkers for myocardial infarction, which provide complementary information to late-gadolinium MR assessment.
Bai W, Peressutti D, Parisot S, et al., 2016, Beyond the AHA 17-segment model: Motion-driven parcellation of the left ventricle, 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 9, 2015, Publisher: Springer, Pages: 13-20, ISSN: 0302-9743
A major challenge for cardiac motion analysis is the highdimensionality of the motion data. Conventionally, the AHA model is used for dimensionality reduction, which divides the left ventricle into 17 segments using criteria based on anatomical structures. In this paper, a novel method is proposed to divide the left ventricle into homogeneous parcels in terms of motion trajectories. We demonstrate that the motion-driven parcellation has good reproducibility and use it for data reduction and motion description on a dataset of 1093 subjects. The resulting motion descriptor achieves high performance on two exemplar applications, namely gender and age predictions. The proposed method has the potential to be applied to groupwise motion analysis.
Oktay O, Tarroni G, Bai W, et al., 2016, Respiratory Motion Correction for 2D Cine Cardiac MR Images using Probabilistic Edge Maps, 43rd Computing in Cardiology Conference (CinC), Publisher: IEEE, Pages: 129-132, ISSN: 2325-8861
Rajchl M, Baxter JSH, Qiu W, et al., 2016, Fast Deformable Image Registration with Non-Smooth Dual Optimization, 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Publisher: IEEE, Pages: 465-472, ISSN: 2160-7508
Oktay O, Bai W, Lee M, et al., 2016, Multi-input cardiac image super-resolution using convolutional neural networks, Pages: 246-254, ISSN: 0302-9743
© Springer International Publishing AG 2016. 3D cardiac MR imaging enables accurate analysis of cardiac morphology and physiology. However,due to the requirements for long acquisition and breath-hold,the clinical routine is still dominated by multi-slice 2D imaging,which hamper the visualization of anatomy and quantitative measurements as relatively thick slices are acquired. As a solution,we propose a novel image super-resolution (SR) approach that is based on a residual convolutional neural network (CNN) model. It reconstructs high resolution 3D volumes from 2D image stacks for more accurate image analysis. The proposed model allows the use of multiple input data acquired from different viewing planes for improved performance. Experimental results on 1233 cardiac short and long-axis MR image stacks show that the CNN model outperforms state-of-the-art SR methods in terms of image quality while being computationally efficient. Also,we show that image segmentation and motion tracking benefits more from SR-CNN when it is used as an initial upscaling method than conventional interpolation methods for the subsequent analysis.
Arslan S, Parisot S, Rueckert D, 2016, Boundary mapping through manifold learning for connectivity-based cortical parcellation, Pages: 115-122, ISSN: 0302-9743
© Springer International Publishing AG 2016. The study of the human connectome is becoming more popular due to its potential to reveal the brain function and structure. A critical step in connectome analysis is to parcellate the cortex into coherent regions that can be used to build graphical models of connectivity. Computing an optimal parcellation is of great importance,as this stage can affect the performance of the subsequent analysis. To this end,we propose a new parcellation method driven by structural connectivity estimated from diffusion MRI. We learn a manifold from the local connectivity properties of an individual subject and identify parcellation boundaries as points in this low-dimensional embedding where the connectivity patterns change. We compute spatially contiguous and non-overlapping parcels from these boundaries after projecting them back to the native cortical surface. Our experiments with a set of 100 subjects show that the proposed method can produce parcels with distinct patterns of connectivity and a higher degree of homogeneity at varying resolutions compared to the state-of-the-art methods,hence can potentially provide a more reliable set of network nodes for connectome analysis.
Ledig C, Kaltwang S, Tolonen A, et al., 2016, Differential dementia diagnosis on incomplete data with latent trees, Pages: 44-52, ISSN: 0302-9743
© Springer International Publishing AG 2016. Incomplete patient data is a substantial problem that is not sufficiently addressed in current clinical research. Many published methods assume both completeness and validity of study data. However,this assumption is often violated as individual features might be unavailable due to missing patient examination or distorted/wrong due to inaccurate measurements or human error. In this work we propose to use the Latent Tree (LT) generative model to address current limitations due to missing data. We show on 491 subjects of a challenging dementia dataset that LT feature estimation is more robust towards incomplete data as compared to mean or Gaussian Mixture Model imputation and has a synergistic effect when combined with common classifiers (we use SVM as example). We show that LTs allow the inclusion of incomplete samples into classifier training. Using LTs,we obtain a balanced accuracy of 62% for the classification of all patients into five distinct dementia types even though 20% of the features are missing in both training and testing data (68% on complete data). Further,we confirm the potential of LTs to detect outlier samples within the dataset.
Oda M, Shimizu N, Karasawa K, et al., 2016, Regression forest-based atlas localization and direction specific atlas generation for pancreas segmentation, Pages: 556-563, ISSN: 0302-9743
© Springer International Publishing AG 2016. This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood vessel information. Previous probabilistic atlas-based pancreas segmentation methods cannot deal with spatial variations that are commonly found in the pancreas well. Also,shape variations are not represented by an averaged atlas. We propose a fully automated pancreas segmentation method that deals with two types of variations mentioned above. The position and size of the pancreas is estimated using a regression forest technique. After localization,a patient-specific probabilistic atlas is generated based on a new image similarity that reflects the blood vessel position and direction information around the pancreas. We segment it using the EM algorithm with the atlas as prior followed by the graph-cut. In evaluation results using 147 CT volumes,the Jaccard index and the Dice overlap of the proposed method were 62.1% and 75.1%,respectively. Although we automated all of the segmentation processes,segmentation results were superior to the other state-of-the-art methods in the Dice overlap.
Coupé P, Wu G, Zhan Y, et al., 2016, Preface, ISBN: 9783319471174
Bowles C, Qin C, Ledig C, et al., 2016, Pseudo-healthy Image Synthesis for White Matter Lesion Segmentation, 1st International Workshop on Simulation and Synthesis in Medical Imaging (SASHIMI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 87-96, ISSN: 0302-9743
Thomaz CE, Amaral V, Gillies DF, et al., 2016, Priori-driven dimensions of face-space: Experiments incorporating eye-tracking information, 9th Biennial ACM Symposium on Eye Tracking Research and Applications (ETRA), Publisher: ASSOC COMPUTING MACHINERY, Pages: 279-282
Murgasova M, Estrin GL, Rutherford M, et al., 2016, DISTORTION CORRECTION IN FETAL EPI USING NON-RIGID REGISTRATION WITH LAPLACIAN CONSTRAINT, IEEE 13th International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 1372-1375, ISSN: 1945-7928
Bozek J, Fitzgibbon S, Wright R, et al., 2016, CONSTRUCTION OF A NEONATAL CORTICAL SURFACE ATLAS USING MULTIMODAL SURFACE MATCHING, IEEE 13th International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 775-778, ISSN: 1945-7928
Kerfoot E, Fovargue L, Rivolo S, et al., 2016, Eidolon: Visualization and computational framework for multi-modal biomedical data analysis, Pages: 425-437, ISSN: 0302-9743
© Springer International Publishing Switzerland 2016. Biomedical research, combining multi-modal image and geometry data, presents unique challenges for data visualization, processing, and quantitative analysis. Medical imaging provides rich information, from anatomical to deformation, but extracting this to a coherent picture across image modalities with preserved quality is not trivial. Addressing these challenges and integrating visualization with image and quantitative analysis results in Eidolon, a platform which can adapt to rapidly changing research workflows. In this paper we outline Eidolon, a software environment aimed at addressing these challenges, and discuss the novel integration of visualization and analysis components. These capabilities are demonstrated through the example of cardiac strain analysis, showing the Eidolon supports and enhances the workflow.
Karasawa K, Kitasaka T, Oda M, et al., 2016, Structure Specific Atlas Generation and Its Application to Pancreas Segmentation from Contrasted Abdominal CT Volumes, International Workshop on Medical Computer Vision, Publisher: SPRINGER INT PUBLISHING AG, Pages: 47-56, ISSN: 0302-9743
Jia D, Shi W, Rueckert D, et al., 2016, A multi-resolution multi-model method for coronary centerline extraction based on minimal path, Pages: 320-328, ISSN: 0302-9743
© Springer International Publishing Switzerland 2016. Extracting centerlines of coronary arteries is challenging but important in clinical applications of cardiac computed tomography angiography (CTA). Since manual annotation of coronary arteries is time-consuming, laborintensive and subject to intra- and inter-variations, we propose a new method to fully automatically extract the coronary centerlines. We first develop a new image filter which generates pixels with salient vessel features within a given window. This filter hence can capture sparsely distributed but important vessel points, enabling the minimal path (MP) process to track the key centerline points at different resolution of the images. Then, we reformulate the filter for multi-resolution fast marching, which not only can speed up the coronary tracking process, but also can help the front propagation to step over the indistinct segments of the coronary artery such as at the locations of stenosis. We embed this scheme into the MP framework to develop a multi-resolution multi-model approach (MMP), where the extracted centerlines from low-resolution MP serve as prior and constraints for the high-resolution process. We evaluated the performance of this method using the Rotterdam CTA training data and the coronary artery algorithm evaluation framework. The average inside of our extraction was 0.51 mm and the overlap was 72.9 %. The mean runtime on the original resolution CTA images was 3.4 min using the MMP method.
Nagara K, Oda H, Nakamura S, et al., 2016, Cascade registration of micro CT volumes taken in multiple resolutions, Pages: 269-280, ISSN: 0302-9743
© Springer International Publishing Switzerland 2016. In this paper, we present a preliminary report of a multiscale registration method between micro-focus X-ray CT (micro CT) volumes taken in different scales. 3D fine structures of target objects can be observed on micro CT volumes, which are difficult to observe on clinical CT volumes. Micro CT scanners can scan specimens in various resolutions. In their high resolution volumes, ultra fine structures of specimens can be observed, while scanned areas are limited to very small. On the other hand, in low resolution volumes, large areas can be captured, while fine structures of specimens are difficult to observe. The fusion volume of the high and low resolution volumes will have benefits of both. Because the difference of resolutions between the high and low resolution volumes may vary greatly, an intermediate resolution volume is required for successful fusion of volumes. To perform such volume fusion, a cascade multi-resolution registration technique is required. To register micro CT volumes that have quite different resolutions, we employ a cascade co-registration technique. In the cascade co-registration process, intermediate resolution volumes are used in a registration process of the high and low resolution volumes. In the registration between two volumes, we apply two steps registration techniques. In the first step, a block division is used to register two resolution volumes. Afterward, we estimate the fine spatial positions relating the registered two volumes using the Powell method. The registration result can be used to generate a fusion volume of the high and low resolution volumes.
Alansary A, Kainz B, Rajchl M, et al., 2016, PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI., CoRR, Vol: abs/1611.07289
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