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Medical Image Analysis

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https://www.readbyqxmd.com/read/27914302/progressive-multi-atlas-label-fusion-by-dictionary-evolution
#1
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)...
November 24, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27907850/automated-annotation-and-quantitative-description-of-ultrasound-videos-of-the-fetal-heart
#2
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
#3
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/27898305/view-aligned-hypergraph-learning-for-alzheimer-s-disease-diagnosis-with-incomplete-multi-modality-data
#4
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...
November 16, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27871000/comparison-of-atlas-based-techniques-for-whole-body-bone-segmentation
#5
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...
November 12, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27894001/multiresolution-extended-free-form-deformations-xffd-for-non-rigid-registration-with-discontinuous-transforms
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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/27816859/cross-contrast-multi-channel-image-registration-using-image-synthesis-for-mr-brain-images
#14
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...
October 22, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27788384/blood-vessel-modeling-for-interactive-simulation-of-interventional-neuroradiology-procedures
#15
E Kerrien, A Yureidini, J Dequidt, C Duriez, R Anxionnat, S Cotin
Endovascular interventions can benefit from interactive simulation in their training phase but also during pre-operative and intra-operative phases if simulation scenarios are based on patient data. A key feature in this context is the ability to extract, from patient images, models of blood vessels that impede neither the realism nor the performance of simulation. This paper addresses both the segmentation and reconstruction of the vasculature from 3D Rotational Angiography data, and adapted to simulation: An original tracking algorithm is proposed to segment the vessel tree while filtering points extracted at the vessel surface in the vicinity of each point on the centerline; then an automatic procedure is described to reconstruct each local unstructured point set as a skeleton-based implicit surface (blobby model)...
October 14, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27770718/a-framework-for-combining-a-motion-atlas-with-non-motion-information-to-learn-clinically-useful-biomarkers-application-to-cardiac-resynchronisation-therapy-response-prediction
#16
Devis Peressutti, Matthew Sinclair, Wenjia Bai, Thomas Jackson, Jacobus Ruijsink, David Nordsletten, Liya Asner, Myrianthi Hadjicharalambous, Christopher A Rinaldi, Daniel Rueckert, Andrew P King
We present a framework for combining a cardiac motion atlas with non-motion data. The atlas represents cardiac cycle motion across a number of subjects in a common space based on rich motion descriptors capturing 3D displacement, velocity, strain and strain rate. The non-motion data are derived from a variety of sources such as imaging, electrocardiogram (ECG) and clinical reports. Once in the atlas space, we apply a novel supervised learning approach based on random projections and ensemble learning to learn the relationship between the atlas data and some desired clinical output...
October 11, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27750189/assisting-the-examination-of-large-histopathological-slides-with-adaptive-forests
#17
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...
October 5, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27723564/ultrasound-contrast-agent-dispersion-and-velocity-imaging-for-prostate-cancer-localization
#18
Ruud Jg van Sloun, Libertario Demi, Arnoud W Postema, Jean Jmch de la Rosette, Hessel Wijkstra, Massimo Mischi
Prostate cancer (PCa) is the second-leading cause of cancer death in men; however, reliable tools for detection and localization are still lacking. Dynamic Contrast Enhanced UltraSound (DCE-US) is a diagnostic tool that is suitable for analysis of vascularization, by imaging an intravenously injected microbubble bolus. The localization of angiogenic vascularization associated with the development of tumors is of particular interest. Recently, methods for the analysis of the bolus convective dispersion process have shown promise to localize angiogenesis...
October 1, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27728873/a-laparoscopy-based-method-for-brdf-estimation-from-in-vivo-human-liver
#19
A L P Nunes, A Maciel, L T Cavazzola, M Walter
While improved visual realism is known to enhance training effectiveness in virtual surgery simulators, the advances on realistic rendering for these simulators is slower than similar simulations for man-made scenes. One of the main reasons for this is that in vivo data is hard to gather and process. In this paper, we propose the analysis of videolaparoscopy data to compute the Bidirectional Reflectance Distribution Function (BRDF) of living organs as an input to physically based rendering algorithms. From the interplay between light and organic matter recorded in video images, we propose the definition of a process capable of establishing the BRDF for inside-the-body organic surfaces...
September 29, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27718462/towards-patient-specific-modeling-of-mitral-valve-repair-3d-transesophageal-echocardiography-derived-parameter-estimation
#20
Fan Zhang, Jingjing Kanik, Tommaso Mansi, Ingmar Voigt, Puneet Sharma, Razvan Ioan Ionasec, Lakshman Subrahmanyan, Ben A Lin, Lissa Sugeng, David Yuh, Dorin Comaniciu, James Duncan
Transesophageal echocardiography (TEE) is routinely used to provide important qualitative and quantitative information regarding mitral regurgitation. Contemporary planning of surgical mitral valve repair, however, still relies heavily upon subjective predictions based on experience and intuition. While patient-specific mitral valve modeling holds promise, its effectiveness is limited by assumptions that must be made about constitutive material properties. In this paper, we propose and develop a semi-automated framework that combines machine learning image analysis with geometrical and biomechanical models to build a patient-specific mitral valve representation that incorporates image-derived material properties...
September 27, 2016: Medical Image Analysis
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