Showing papers in "Medical Image Analysis in 2013"

TL;DR: This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations.

TL;DR: The state of the art in this important field of respiratory motion modelling is summarized and in the process the key papers that have driven its advance are highlighted.

TL;DR: The complexity of lesions segmentation is described, the automatic MS lesion segmentation methods found, and the validation methods applied are reviewed, to evaluate the state of the art in automated multiple sclerosis lesion segmentsation.

TL;DR: The state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery is reviewed and the technical challenges and future perspectives towards clinical translation are discussed.

TL;DR: A new local ranking strategy for template selection based on the locally normalised cross correlation (LNCC) and an extension to the classical STAPLE algorithm by Warfield et al. (2004) are proposed, which is referred to as STEPS for Similarity and Truth Estimation for Propagated Segmentations, which obtains more accurate segmentations even when using only a third of the templates, reducing the dependence on large template databases.

TL;DR: A new, continuous parametrization of the anatomy localization problem, which allows it to be addressed effectively by multi-class random regression forests, and is more accurate and robust than techniques based on efficient multi-atlas registration and template-based nearest-neighbor detection.

TL;DR: In this article, a blind deconvolution technique for BOLD-fMRI signal is proposed, where point processes corresponding to signal fluctuations with a given signature are individuated, and a region-specific hemodynamic response function (HRF) is extracted and used to deconvolve BOLD signal.

TL;DR: NLS reformulates the STAPLE framework from a non-local means perspective in order to learn what label an atlas would have observed, given perfect correspondence, and seamlessly integrates intensity into the estimation process.

TL;DR: Overall, it is shown that averaging improves quality of tractography, sharp angular ODF profiles helps tractography and deterministic tractography produces less invalid tracts which leads to better connectivity results than probabilistic tractography.

TL;DR: Results show that some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that automatic lumen segmentation is possible with a precision similar to that obtained by experts.

TL;DR: A sparse-based super-resolution method, adapted for easily including prior knowledge, which couples up high and low frequency information so that a high-resolution version of a low-resolution brain MR image is generated, shown to outperform a recent state-of-the-art algorithm.

TL;DR: Experimental results indicate that the presented method is superior to threshold and Bayesian methods commonly used in PET image segmentation, is more accurate and robust compared to the other PET-CT segmentation methods recently published in the literature, and also it is general in the sense of simultaneously segmenting multiple scans in real-time with high accuracy needed in routine clinical use.

TL;DR: A benchmarking framework for the validation of cardiac motion analysis algorithms is presented and good agreement was found between the different modalities and methodologies, except for radial strain that showed a high variability in cases of lower image quality.

TL;DR: This paper proposes several methods for automatic surgical gesture classification from video data and shows that methods based on video data perform equally well, if not better, than state-of-the-art approaches based on kinematic data.

TL;DR: A fast segmentation method based on a new variant of spectral graph theory named diffusion maps that demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients is introduced.

TL;DR: A novel method is presented that combines Marginal Space Learning (MSL), a recently introduced concept for efficient discriminative object detection, with a generative anatomical network that incorporates relative pose information for the detection of multiple objects to simultaneously detect and label the spinal disks.

TL;DR: In this article, a bag-of-visual-words (BoW) representation of local features was used to classify NBI images of colorectal tumors into three types (A, B, and C3) based on the NBI magnification findings.

TL;DR: An automatic drusen segmentation method for SD-OCT retinal images, which leverages a priori knowledge of normal retinal morphology and anatomical features to produce useful quantitative imaging biomarkers to follow this disease and predict patient outcome, and a novel method of retinal projection to generate an en face retinal image based on the RPE extraction.

TL;DR: This article presents a method to analyse the fibre architecture of the left ventricle (LV) using shape-based transformation into a normalised Prolate Spheroidal coordinate frame and shows the advantages of using curvilinear coordinates both for the anaylsis and the interpolation of cardiac DTI information.

TL;DR: Feature-Based Alignment (FBA) as mentioned in this paperBA is a general method for efficient and robust model-to-image alignment, where features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution.

TL;DR: Part-based model of global landmark topology to select the final landmark positions and random Forest classifiers for every landmark as a pre-filtering stage are presented.

TL;DR: The results suggest that following further rigorous validation, SeSMiK-GE could be developed into a powerful diagnostic and prognostic tool for detection and grading of CaP in vivo and in helping to determine the appropriate treatment option.

TL;DR: It is shown that an efficient use of the CS theory enables to drastically reduce the number of measurements commonly used in dMRI acquisitions, which opens an attractive perspective to measure the diffusion signals in white matter within a reduced acquisition time.

TL;DR: A phase contrast microscope image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis are proposed.

TL;DR: Results show that the new strategy for modelling sliding conditions when registering 3D images in a piecewise-diffeomorphic framework estimates accurate mappings of entire 3D thoracic image volumes that exhibit a sliding motion, as opposed to conventional registration methods which are not capable of capturing discontinuous deformations at theThoracic cage boundary.

TL;DR: This paper presents a method for estimating diastolic mechanical parameters of the left ventricle (LV) from cine and tagged MRI measurements and LV cavity pressure recordings, separating the passive myocardial constitutive properties and diastsolic residual AT.

TL;DR: NAFs are introduced, a supervised learning algorithm providing a general and efficient approach for the task of approximate nearest neighbour retrieval for arbitrary distances and able to efficiently infer nearest neighbours of an out-of-sample image, even when the original distance is based on semantic information.

TL;DR: It is concluded that a completely automated registration of volume images with 2D mammograms is feasible and within the clinically relevant range and thus beneficial for multimodal diagnosis.

TL;DR: A novel segmentation algorithm is proposed that improves lung segmentation for cases in which the lung has a unique shape and pathologies such as pleural effusion by incorporating multiple shapes and prior information on neighbor structures in a graph cut framework.

TL;DR: A new formulation of the random walks method, coined as guided random walks, in which prior knowledge is integrated seamlessly is introduced, which is shown to be accurate if sufficient number of seeds is provided.