journal
MENU ▼
Read by QxMD icon Read
search

Medical Image Analysis

journal
https://www.readbyqxmd.com/read/28011374/robust-cranial-cavity-segmentation-in-ct-and-ct-perfusion-images-of-trauma-and-suspected-stroke-patients
#1
Ajay Patel, Bram van Ginneken, Frederick J A Meijer, Ewoud J van Dijk, Mathias Prokop, Rashindra Manniesing
A robust and accurate method is presented for the segmentation of the cranial cavity in computed tomography (CT) and CT perfusion (CTP) images. The method consists of multi-atlas registration with label fusion followed by a geodesic active contour levelset refinement of the segmentation. Pre-registration atlas selection based on differences in anterior skull anatomy reduces computation time whilst optimising performance. The method was evaluated on a large clinical dataset of 573 acute stroke and trauma patients that received a CT or CTP in our hospital in the period February 2015-December 2015...
December 13, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/28006726/perfusion-deconvolution-in-dsc-mri-with-dispersion-compliant-bases
#2
Marco Pizzolato, Timothé Boutelier, Rachid Deriche
Perfusion imaging of the brain via Dynamic Susceptibility Contrast MRI (DSC-MRI) allows tissue perfusion characterization by recovering the tissue impulse response function and scalar parameters such as the cerebral blood flow (CBF), blood volume (CBV), and mean transit time (MTT). However, the presence of bolus dispersion causes the data to reflect macrovascular properties, in addition to tissue perfusion. In this case, when performing deconvolution of the measured arterial and tissue concentration time-curves it is only possible to recover the effective, i...
December 6, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27940226/direct-and-simultaneous-estimation-of-cardiac-four-chamber-volumes-by-multioutput-sparse-regression
#3
Xiantong Zhen, Heye Zhang, Ali Islam, Mousumi Bhaduri, Ian Chan, Shuo Li
Cardiac four-chamber volume estimation serves as a fundamental and crucial role in clinical quantitative analysis of whole heart functions. It is a challenging task due to the huge complexity of the four chambers including great appearance variations, huge shape deformation and interference between chambers. Direct estimation has recently emerged as an effective and convenient tool for cardiac ventricular volume estimation. However, existing direct estimation methods were specifically developed for one single ventricle, i...
November 30, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27940225/migration-and-interaction-tracking-for-quantitative-analysis-of-phagocyte-pathogen-confrontation-assays
#4
Susanne Brandes, Stefanie Dietrich, Kerstin Hünniger, Oliver Kurzai, Marc Thilo Figge
Invasive fungal infections are emerging as a significant health risk for humans. The innate immune system is the first line of defense against invading micro-organisms and involves the recruitment of phagocytes, which engulf and kill pathogens, to the site of infection. To gain a quantitative understanding of the interplay between phagocytes and fungal pathogens, live-cell imaging is a modern approach to monitor the dynamic process of phagocytosis in time and space. However, this requires the processing of large amounts of video data that is tedious to be performed manually...
November 25, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27907850/automated-annotation-and-quantitative-description-of-ultrasound-videos-of-the-fetal-heart
#5
Christopher P Bridge, Christos Ioannou, J Alison Noble
Interpretation of ultrasound videos of the fetal heart is crucial for the antenatal diagnosis of congenital heart disease (CHD). We believe that automated image analysis techniques could make an important contribution towards improving CHD detection rates. However, to our knowledge, no previous work has been done in this area. With this goal in mind, this paper presents a framework for tracking the key variables that describe the content of each frame of freehand 2D ultrasound scanning videos of the healthy fetal heart...
November 19, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27898306/dcan-deep-contour-aware-networks-for-object-instance-segmentation-from-histology-images
#6
Hao Chen, Xiaojuan Qi, Lequan Yu, Qi Dou, Jing Qin, Pheng-Ann Heng
In histopathological image analysis, the morphology of histological structures, such as glands and nuclei, has been routinely adopted by pathologists to assess the malignancy degree of adenocarcinomas. Accurate detection and segmentation of these objects of interest from histology images is an essential prerequisite to obtain reliable morphological statistics for quantitative diagnosis. While manual annotation is error-prone, time-consuming and operator-dependant, automated detection and segmentation of objects of interest from histology images can be very challenging due to the large appearance variation, existence of strong mimics, and serious degeneration of histological structures...
November 16, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27894001/multiresolution-extended-free-form-deformations-xffd-for-non-rigid-registration-with-discontinuous-transforms
#7
Rui Hua, Jose M Pozo, Zeike A Taylor, Alejandro F Frangi
Image registration is an essential technique to obtain point correspondences between anatomical structures from different images. Conventional non-rigid registration methods assume a continuous and smooth deformation field throughout the image. However, the deformation field at the interface of different organs is not necessarily continuous, since the organs may slide over or separate from each other. Therefore, imposing continuity and smoothness ubiquitously would lead to artifacts and increased errors near the discontinuity interface...
November 9, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27870999/sparse-bayesian-registration-of-medical-images-for-self-tuning-of-parameters-and-spatially-adaptive-parametrization-of-displacements
#8
Loïc Le Folgoc, Hervé Delingette, Antonio Criminisi, Nicholas Ayache
We extend Bayesian models of non-rigid image registration to allow not only for the automatic determination of registration parameters (such as the trade-off between image similarity and regularization functionals), but also for a data-driven, multiscale, spatially adaptive parametrization of deformations. Adaptive parametrizations have been used with success to promote both the regularity and accuracy of registration schemes, but so far on non-probabilistic grounds - either as part of multiscale heuristics, or on the basis of sparse optimization...
November 9, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27842236/improving-airway-segmentation-in-computed-tomography-using-leak-detection-with-convolutional-networks
#9
Jean-Paul Charbonnier, Eva M van Rikxoort, Arnaud A A Setio, Cornelia M Schaefer-Prokop, Bram van Ginneken, Francesco Ciompi
We propose a novel method to improve airway segmentation in thoracic computed tomography (CT) by detecting and removing leaks. Leak detection is formulated as a classification problem, in which a convolutional network (ConvNet) is trained in a supervised fashion to perform the classification task. In order to increase the segmented airway tree length, we take advantage of the fact that multiple segmentations can be extracted from a given airway segmentation algorithm by varying the parameters that influence the tree length and the amount of leaks...
November 4, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27865153/efficient-multi-scale-3d-cnn-with-fully-connected-crf-for-accurate-brain-lesion-segmentation
#10
Konstantinos Kamnitsas, Christian Ledig, Virginia F J Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, Ben Glocker
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data...
October 29, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27816861/deep-learning-for-automated-skeletal-bone-age-assessment-in-x-ray-images
#11
C Spampinato, S Palazzo, D Giordano, M Aldinucci, R Leonardi
Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders in children. It is generally performed by radiological examination of the left hand by using either the Greulich and Pyle (G&P) method or the Tanner-Whitehouse (TW) one. However, both clinical procedures show several limitations, from the examination effort of radiologists to (most importantly) significant intra- and inter-operator variability. To address these problems, several automated approaches (especially relying on the TW method) have been proposed; nevertheless, none of them has been proved able to generalize to different races, age ranges and genders...
October 29, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27816860/unsupervised-boundary-delineation-of-spinal-neural-foramina-using-a-multi-feature-and-adaptive-spectral-segmentation
#12
Xiaoxu He, Heye Zhang, Mark Landis, Manas Sharma, James Warrington, Shuo Li
As a common disease in the elderly, neural foramina stenosis (NFS) brings a significantly negative impact on the quality of life due to its symptoms including pain, disability, fall risk and depression. Accurate boundary delineation is essential to the clinical diagnosis and treatment of NFS. However, existing clinical routine is extremely tedious and inefficient due to the requirement of physicians' intensively manual delineation. Automated delineation is highly needed but faces big challenges from the complexity and variability in neural foramina images...
October 29, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27816858/automatic-apical-view-classification-of-echocardiograms-using-a-discriminative-learning-dictionary
#13
Hanan Khamis, Grigoriy Zurakhov, Vered Azar, Adi Raz, Zvi Friedman, Dan Adam
As part of striving towards fully automatic cardiac functional assessment of echocardiograms, automatic classification of their standard views is essential as a pre-processing stage. The similarity among three of the routinely acquired longitudinal scans: apical two-chamber (A2C), apical four-chamber (A4C) and apical long-axis (ALX), and the noise commonly inherent to these scans - make the classification a challenge. Here we introduce a multi-stage classification algorithm that employs spatio-temporal feature extraction (Cuboid Detector) and supervised dictionary learning (LC-KSVD) approaches to uniquely enhance the automatic recognition and classification accuracy of echocardiograms...
October 24, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27788448/erratum-to-predicting-infant-cortical-surface-development-using-a-4d-varifold-based-learning-framework-and-local-topography-based-shape-morphing-med-image-anal-28-2016-1-12
#14
Islem Rekik, Gang Li, Weili Lin, Dinggang Shen
No abstract text is available yet for this article.
October 24, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27914302/progressive-multi-atlas-label-fusion-by-dictionary-evolution
#15
Yantao Song, Guorong Wu, Khosro Bahrami, Quansen Sun, Dinggang Shen
Accurate segmentation of anatomical structures in medical images is important in recent imaging based studies. In the past years, multi-atlas patch-based label fusion methods have achieved a great success in medical image segmentation. In these methods, the appearance of each input image patch is first represented by an atlas patch dictionary (in the image domain), and then the latent label of the input image patch is predicted by applying the estimated representation coefficients to the corresponding anatomical labels of the atlas patches in the atlas label dictionary (in the label domain)...
February 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/27898305/view-aligned-hypergraph-learning-for-alzheimer-s-disease-diagnosis-with-incomplete-multi-modality-data
#16
Mingxia Liu, Jun Zhang, Pew-Thian Yap, Dinggang Shen
Effectively utilizing incomplete multi-modality data for the diagnosis of Alzheimer's disease (AD) and its prodrome (i.e., mild cognitive impairment, MCI) remains an active area of research. Several multi-view learning methods have been recently developed for AD/MCI diagnosis by using incomplete multi-modality data, with each view corresponding to a specific modality or a combination of several modalities. However, existing methods usually ignore the underlying coherence among views, which may lead to sub-optimal learning performance...
February 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/27871000/comparison-of-atlas-based-techniques-for-whole-body-bone-segmentation
#17
Hossein Arabi, Habib Zaidi
We evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross validation procedure...
February 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/27816859/cross-contrast-multi-channel-image-registration-using-image-synthesis-for-mr-brain-images
#18
Min Chen, Aaron Carass, Amod Jog, Junghoon Lee, Snehashis Roy, Jerry L Prince
Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation...
February 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/27750189/assisting-the-examination-of-large-histopathological-slides-with-adaptive-forests
#19
Loïc Peter, Diana Mateus, Pierre Chatelain, Denis Declara, Noemi Schworm, Stefan Stangl, Gabriele Multhoff, Nassir Navab
The examination of biopsy samples plays a central role in the diagnosis and staging of numerous diseases, including most cancer types. However, because of the large size of the acquired images, the localization and quantification of diseased portions of a tissue is usually time-consuming, as pathologists must scroll through the whole slide to look for objects of interest which are often only scarcely distributed. In this work, we introduce an approach to facilitate the visual inspection of large digital histopathological slides...
January 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/27662597/probabilistic-tractography-using-lasso-bootstrap
#20
Chuyang Ye, Jerry L Prince
Diffusion magnetic resonance imaging (dMRI) can be used for noninvasive imaging of white matter tracts. Using fiber tracking, which propagates fiber streamlines according to fiber orientations (FOs) computed from dMRI, white matter tracts can be reconstructed for investigation of brain diseases and the brain connectome. Because of image noise, probabilistic tractography has been proposed to characterize uncertainties in FO estimation. Bootstrap provides a nonparametric approach to the estimation of FO uncertainties and residual bootstrap has been used for developing probabilistic tractography...
January 2017: Medical Image Analysis
journal
journal
32848
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"