Abstract:
:We propose two information theoretic similarity measures that allow to incorporate tissue class information in non-rigid image registration. The first measure assumes that tissue class probabilities have been assigned to each of the images to be registered by prior segmentation of both of them. One image is then non-rigidly deformed to match the other such that the fuzzy overlap of corresponding voxel object labels becomes similar to the ideal case whereby the tissue probability maps of both images are identical. Image similarity is assessed during registration by the divergence between the ideal and actual joint class probability distributions of both images. A second registration measure is proposed that applies in case a segmentation is available for only one of the images, for instance an atlas image that is to be matched to a study image to guide the segmentation thereof. Intensities in one image are matched to the fuzzy class labels in the other image by minimizing the conditional entropy of the intensities in the first image given the class labels in the second image. We derive analytic expressions for the gradient of each measure with respect to individual voxel displacements to derive a force field that drives the registration process, which is regularized by a viscous fluid model. The performance of the class-based measures is evaluated in the context of non-rigid inter-subject registration and atlas-based segmentation of MR brain images and compared with maximization of mutual information using only intensity information. Our results demonstrate that incorporation of class information in the registration measure significantly improves the overlap between corresponding tissue classes after non-rigid matching. The methods proposed here open new perspectives for integrating segmentation and registration in a single process, whereby the output of one is used to guide the other.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
D'Agostino E,Maes F,Vandermeulen D,Suetens Pdoi
10.1016/j.media.2005.03.004subject
Has Abstractpub_date
2006-06-01 00:00:00pages
413-31issue
3eissn
1361-8415issn
1361-8423pii
S1361-8415(05)00042-3journal_volume
10pub_type
杂志文章abstract::In this study, we propose a novel anomaly detection model targeting subtle brain lesions in multiparametric MRI. To compensate for the lack of annotated data adequately sampling the heterogeneity of such pathologies, we cast this problem as an outlier detection problem and introduce a novel configuration of unsupervis...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2019.101618
更新日期:2020-02-01 00:00:00
abstract::In-silico continuum simulations of organ and tissue scale physiology often require a discretisation or mesh of the solution domain. Cubic Hermite meshes provide a smooth representation of anatomy that is well-suited for simulating large deformation mechanics. Models of organ mechanics and deformation have demonstrated...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2011.06.010
更新日期:2011-12-01 00:00:00
abstract::After two decades of increasing interest and research activity, computer-assisted diagnostic approaches are reaching the stage where more routine deployment in clinical practice is becoming a possibility [Kruppinski, E.A., 2004. Computer-aided detection in clinical environment: Benefits and challenges for radiologists...
journal_title:Medical image analysis
pub_type: 杂志文章,评审
doi:10.1016/j.media.2005.06.003
更新日期:2006-04-01 00:00:00
abstract::Many cardiac pathologies are reflected in abnormal myocardial deformation, accessible through magnetic resonance tagging (MRT). Interpretation of the MRT data is difficult, since the relation between pathology and deformation is not straightforward. Mathematical models of cardiac mechanics could be used to translate m...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2006.04.001
更新日期:2006-08-01 00:00:00
abstract::Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T1 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different acquisition parameters (or a...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2015.12.004
更新日期:2016-04-01 00:00:00
abstract::In this paper, we propose a novel continuum finite strain formulation of the equilibrium gap regularization for image registration. The equilibrium gap regularization essentially penalizes any deviation from the solution of a hyperelastic body in equilibrium with arbitrary loads prescribed at the boundary. It thus rep...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2018.07.007
更新日期:2018-12-01 00:00:00
abstract::Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Currently, many multi-task learning approaches have been proposed to predict the disease progression at the early stage using longitudinal data, with each task corresponding to a pa...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2019.01.007
更新日期:2019-04-01 00:00:00
abstract::Classification of digital pathology images is imperative in cancer diagnosis and prognosis. Recent advancements in deep learning and computer vision have greatly benefited the pathology workflow by developing automated solutions for classification tasks. However, the cost and time for acquiring high quality task-speci...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2020.101859
更新日期:2021-01-01 00:00:00
abstract::Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly being used for the detection and diagnosis of breast cancer. Compared to mammography, DCE-MRI provides higher sensitivity, however its specificity is variable. Moreover, DCE-MRI data analysis is time consuming and depends on reader expertis...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2014.12.001
更新日期:2015-02-01 00:00:00
abstract::The problem of respiratory motion has proved a serious obstacle in developing techniques to acquire images or guide interventions in abdominal and thoracic organs. Motion models offer a possible solution to these problems, and as a result the field of respiratory motion modelling has become an active one over the past...
journal_title:Medical image analysis
pub_type: 杂志文章,评审
doi:10.1016/j.media.2012.09.005
更新日期:2013-01-01 00:00:00
abstract::The ability to predict patient-specific soft tissue deformations is key for computer-integrated surgery systems and the core enabling technology for a new era of personalized medicine. Element-Free Galerkin (EFG) methods are better suited for solving soft tissue deformation problems than the finite element method (FEM...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2019.06.004
更新日期:2019-08-01 00:00:00
abstract::In this work a new statistic deformable model for 3D segmentation of anatomical organs in medical images is proposed. A statistic discriminant snake performs a supervised learning of the object boundary in an image slice to segment the next slice of the image sequence. Each part of the object boundary is projected in ...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/s1361-8415(03)00014-8
更新日期:2003-09-01 00:00:00
abstract::Automated ventricle volume estimation (AVVE) on cardiac magnetic resonance (CMR) images is very important for clinical cardiac disease diagnosis. However, current AVVE methods ignore the error correction for the estimated volume. This results in clinically intolerable ventricle volume estimation error and further lead...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2020.101723
更新日期:2020-08-01 00:00:00
abstract::Creating a feature-preserving average of three dimensional anatomical surfaces extracted from volume image data is a complex task. Unlike individual images, averages present right-left symmetry and smooth surfaces which give insight into typical proportions. Averaging multiple biological surface images requires carefu...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/s1361-8415(00)00031-1
更新日期:2000-12-01 00:00:00
abstract::Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatment of prostate cancer. However, it is a very challenging task due to unclear boundaries, large intra- and inter-patient shape variability, and uncertain existence of bo...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2019.03.003
更新日期:2019-05-01 00:00:00
abstract::Although there are many algorithms available in the literature aimed at segmentation and model reconstruction of 3D angiographic images, many are focused on characterizing only a part of the vascular network. This study is motivated by the recent emerging prospects of whole-organ simulations in coronary hemodynamics, ...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2007.06.012
更新日期:2007-12-01 00:00:00
abstract::Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model proper...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2020.101950
更新日期:2020-12-25 00:00:00
abstract::We describe a new algorithm for non-rigid registration capable of estimating a constrained dense displacement field from multi-modal image data. We applied this algorithm to capture non-rigid deformation between digital images of histological slides and digital flat-bed scanned images of cryotomed sections of the lary...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2005.04.003
更新日期:2005-12-01 00:00:00
abstract::A new method is proposed to quantify the myocardial motion from both 2D C(ine)-MRI and T(agged)-MRI sequences. The tag pattern offers natural landmarks within the image that makes it possible to accurately quantify the motion within the myocardial wall. Therefore, several methods have been proposed for T-MRI. However,...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2019.06.016
更新日期:2019-10-01 00:00:00
abstract::Real-time cardiac MRI appears as a promising technique to evaluate the mechanical function of the heart. However, ultra-fast MRI acquisitions come with an important signal-to-noise ratio (SNR) penalty, which drastically reduces the image quality. Hence, a real-time denoising approach would be desirable for SNR amelior...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2009.05.006
更新日期:2009-08-01 00:00:00
abstract::The accurate localization of anatomical landmarks is a challenging task, often solved by domain specific approaches. We propose a method for the automatic localization of landmarks in complex, repetitive anatomical structures. The key idea is to combine three steps: (1) a classifier for pre-filtering anatomical landma...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2013.02.004
更新日期:2013-12-01 00:00:00
abstract::A stochastic finite element framework is presented for the simultaneous estimation of the cardiac kinematic functions and material model parameters from periodic medical image sequences. While existing biomechanics studies of the myocardial material constitutive laws have assumed known tissue kinematic measurements, a...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/s1361-8415(03)00066-5
更新日期:2003-12-01 00:00:00
abstract::A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2019.01.001
更新日期:2019-04-01 00:00:00
abstract::With the development of deep learning, its application in diagnosis of benign and malignant thyroid nodules has been widely concerned. However, it is difficult to obtain medical images, resulting in insufficient number of data, which contradicts the large amount of data required for acquiring effective deep learning d...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2020.101819
更新日期:2021-01-01 00:00:00
abstract::In this paper, we propose a new strategy for modelling sliding conditions when registering 3D images in a piecewise-diffeomorphic framework. More specifically, our main contribution is the development of a mathematical formalism to perform Large Deformation Diffeomorphic Metric Mapping registration with sliding condit...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2012.10.001
更新日期:2013-02-01 00:00:00
abstract::Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissu...
journal_title:Medical image analysis
pub_type: 社论
doi:10.1016/j.media.2016.06.031
更新日期:2016-10-01 00:00:00
abstract::Clinical acceptance of 3-D OCT retinal imaging brought rapid development of quantitative 3-D analysis of retinal layers, vasculature, retinal lesions as well as facilitated new research in retinal diseases. One of the cornerstones of many such analyses is segmentation and thickness quantification of retinal layers and...
journal_title:Medical image analysis
pub_type: 社论
doi:10.1016/j.media.2016.06.001
更新日期:2016-10-01 00:00:00
abstract::In this paper, we present a framework to estimate local ventricular myocardium contractility using clinical MRI, a heart model and data assimilation. First, we build a generic anatomical model of the ventricles including muscle fibre orientations and anatomical subdivisions. Then, this model is deformed to fit a clini...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2006.04.002
更新日期:2006-08-01 00:00:00
abstract::An automated vendor-independent system for dose monitoring in computed tomography (CT) medical examinations involving ionizing radiation is presented in this paper. The system provides precise size-specific dose estimates (SSDE) following the American Association of Physicists in Medicine regulations. Our dose managem...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2020.101898
更新日期:2021-02-01 00:00:00
abstract::This paper describes a method for building efficient representations of large sets of brain images. Our hypothesis is that the space spanned by a set of brain images can be captured, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifol...
journal_title:Medical image analysis
pub_type: 杂志文章
doi:10.1016/j.media.2010.05.008
更新日期:2010-10-01 00:00:00