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Showing papers in "Medical Image Analysis in 2016"


Journal ArticleDOI
TL;DR: This review discusses developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective, and reflects on future opportunities for the quantitation of histopathology.

693 citations


Journal ArticleDOI
TL;DR: In this paper, the authors employed deep learning algorithms combined with deformable models to develop and evaluate a fully automatic LV segmentation tool from short-axis cardiac MRI datasets, which outperformed the state-of-the-art methods.

483 citations


Journal ArticleDOI
TL;DR: A whole heart segmentation method that employs multi-modality atlases from MRI and CT and adopts a new label fusion algorithm which is based on the proposed multi-scale patch (MSP) strategy and a new global atlas ranking scheme is presented for cardiac MRI.

348 citations


Journal ArticleDOI
TL;DR: This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results.

264 citations


Journal ArticleDOI
TL;DR: It may be concluded that the field of medical image registration has evolved, but still is in need of further development in various aspects.

248 citations


Journal ArticleDOI
TL;DR: CAC can be accurately automatically identified and quantified in CCTA using the proposed pattern recognition method, which might obviate the need to acquire a dedicated CSCT scan for CAC scoring, and thus reduce the CT radiation dose received by patients.

246 citations


Journal ArticleDOI
TL;DR: Based on the quantitative evaluation results, it is believed automatic dental radiography analysis is still a challenging and unsolved problem and the datasets and the evaluation software are made available to the research community, further encouraging future developments in this field.

246 citations


Journal ArticleDOI
TL;DR: The proposed method uses a coarse-to-fine analysis of the localized characteristics in pathology images to automatically differentiate between the two cancer subtypes and showed high stability and robustness to parameter variation.

172 citations


Journal ArticleDOI
TL;DR: A graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions and outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets.

150 citations


Journal ArticleDOI
TL;DR: The groupwise registration method with a similarity measure based on PCA is the preferred technique for compensating misalignments in qMRI.

125 citations


Journal ArticleDOI
TL;DR: A general, fully learning-based framework for direct bi-ventricular volume estimation, which removes user inputs and unreliable assumptions, and largely outperforms existing direct methods on a larger dataset of 100 subjects including both healthy and diseased cases with twice the number of subjects used in previous methods.

Journal ArticleDOI
TL;DR: The most popular platforms for navigation technology are reviewed, and an effective way to take advantage of them is shown through an example surgical navigation application.

Journal ArticleDOI
TL;DR: This work introduces an efficient way of processing wave images acquired by multifrequency magnetic resonance elastography (MMRE), which relies on wave number reconstruction at different harmonic frequencies followed by their amplitude-weighted averaging prior to inversion to reveal variations in tissue elasticity in a tomographic fashion.

Journal ArticleDOI
TL;DR: A novel framework for estimating the hyper-connectivity network of brain functions and then using this hyper-network for brain disease diagnosis is proposed, which can not only improve the classification performance, but also help discover disease-related biomarkers important for disease diagnosis.

Journal ArticleDOI
TL;DR: A tree-structured discrete graphical model is introduced that is used to select and label a set of non-overlapping regions in the image by a global optimization of a classification score, and the performance of the model can be improved by considering a proxy problem for learning the surface that allows better selection of the extremal regions.

Journal ArticleDOI
TL;DR: In this paper, the authors describe recently developed technologies for better handling of image information: photorealistic visualization of medical images with Cinematic Rendering, artificial agents for in-depth image understanding, support for minimally invasive procedures, and patient-specific computational models with enhanced predictive power.

Journal ArticleDOI
TL;DR: The benchmarking evaluation framework can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV, with the exception of the Full-Width-at-Half-Maximum (FWHM) fixed-thresholding method.

Journal ArticleDOI
TL;DR: Theoretical features in model-based and tomographic reconstruction of coronary arteries, and the potential role of reconstructions in clinical decision making and interventional guidance are discussed, and areas for future research are highlighted.

Journal ArticleDOI
Jürgen Weese1, Cristian Lorenz1
TL;DR: Algorithms for analyzing heterogeneous image data, anatomical and organ models play a crucial role in many applications, and algorithms to construct patient-specific models from medical images with a minimum of user interaction are needed.

Journal ArticleDOI
TL;DR: An approach that enables accurate voxel-wise deformable registration of high-resolution 3D images without the need for intermediate image warping or a multi-resolution scheme is proposed, and significant improvements in registration accuracy are shown when using the additional information provided by the registration uncertainty estimates.

Journal ArticleDOI
TL;DR: This paper proposes a novel diffusion MRI denoising technique that can be used on all existing data, without adding to the scanning time, and improves the visual quality of the data and reduces the number of spurious tracts when compared to the noisy acquisition.

Journal ArticleDOI
TL;DR: A fully automated framework for image-level tortuosity estimation, consisting of a hybrid segmentation method and a highly adaptable, definition-free tortuosity estimation algorithm, based on a novel tortUosity estimation paradigm in which discriminative, multi-scale features can be automatically learned for specific anatomical objects and diseases.

Journal ArticleDOI
TL;DR: It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transforms and the Shearlet transform.

Journal ArticleDOI
TL;DR: This paper proposes a prediction system primarily using radiomic features extracted from FDG-PET images, which aims to improve the prediction accuracy, and reduce the imprecision and overlaps between different classes (treatment outcomes) in a selected feature subspace.

Journal ArticleDOI
TL;DR: Minimal user interaction is needed for a good segmentation of the placenta and co-segmentation of multiple volumes outperforms single sparse volume based method.

Journal ArticleDOI
TL;DR: It is advocated that the scale of image retrieval systems should be significantly increased at which interactive systems can be effective for knowledge discovery in potentially large databases of medical images.

Journal ArticleDOI
TL;DR: This work will present its own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field.

Journal ArticleDOI
TL;DR: An overall scheme of the computer based process for planning a bone fracture reduction is presented, and its main steps, the most common proposed techniques and their main shortcomings are detailed.

Journal ArticleDOI
TL;DR: What are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ("Open Science"); and how the quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision are discussed.

Journal ArticleDOI
TL;DR: A new computer-aided method to detect lesion images and provide worthwhile guidance for improving the efficiency and accuracy of gastrointestinal disease diagnosis and is a good prospect for clinical application.