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Medical Image Analysis

Michela Antonelli, M Jorge Cardoso, Edward W Johnston, Mrishta Brizmohun Appayya, Benoit Presles, Marc Modat, Shonit Punwani, Sebastien Ourselin
Multi-Atlas based Segmentation (MAS) algorithms have been successfully applied to many medical image segmentation tasks, but their success relies on a large number of atlases and good image registration performance. Choosing well-registered atlases for label fusion is vital for an accurate segmentation. This choice becomes even more crucial when the segmentation involves organs characterized by a high anatomical and pathological variability. In this paper, we propose a new genetic atlas selection strategy (GAS) that automatically chooses the best subset of atlases to be used for segmenting the target image, on the basis of both image similarity and segmentation overlap...
November 19, 2018: Medical Image Analysis
Vimal Chandran, Ghislain Maquer, Thomas Gerig, Philippe Zysset, Mauricio Reyes
Knowledge about the thickness of the cortical bone is of high interest for fracture risk assessment. Most finite element model solutions overlook this information because of the coarse resolution of the CT images. To circumvent this limitation, a three-steps approach is proposed. 1) Two initial surface meshes approximating the outer and inner cortical surfaces are generated via a shape regression based on morphometric features and statistical shape model parameters. 2) The meshes are then corrected locally using a supervised learning model build from image features extracted from pairs of QCT (0...
November 16, 2018: Medical Image Analysis
Hassan Al Hajj, Mathieu Lamard, Pierre-Henri Conze, Soumali Roychowdhury, Xiaowei Hu, Gabija Maršalkaitė, Odysseas Zisimopoulos, Muneer Ahmad Dedmari, Fenqiang Zhao, Jonas Prellberg, Manish Sahu, Adrian Galdran, Teresa Araújo, Duc My Vo, Chandan Panda, Navdeep Dahiya, Satoshi Kondo, Zhengbing Bian, Arash Vahdat, Jonas Bialopetravičius, Evangello Flouty, Chenhui Qiu, Sabrina Dill, Anirban Mukhopadhyay, Pedro Costa, Guilherme Aresta, Senthil Ramamurthy, Sang-Woong Lee, Aurélio Campilho, Stefan Zachow, Shunren Xia, Sailesh Conjeti, Danail Stoyanov, Jogundas Armaitis, Pheng-Ann Heng, William G Macready, Béatrice Cochener, Gwenolé Quellec
Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries...
November 16, 2018: Medical Image Analysis
T Lossau, H Nickisch, T Wissel, R Bippus, H Schmitt, M Morlock, M Grass
Excellent image quality is a primary prerequisite for diagnostic non-invasive coronary CT angiography. Artifacts due to cardiac motion may interfere with detection and diagnosis of coronary artery disease and render subsequent treatment decisions more difficult. We propose deep-learning-based measures for coronary motion artifact recognition and quantification in order to assess the diagnostic reliability and image quality of coronary CT angiography images. More specifically, the application, steering and evaluation of motion compensation algorithms can be triggered by these measures...
November 15, 2018: Medical Image Analysis
Yang Li, Jingyu Liu, Xinqiang Gao, Biao Jie, Minjeong Kim, Pew-Thian Yap, Chong-Yaw Wee, Dinggang Shen
Recent works have shown that hyper-networks derived from blood-oxygen-level-dependent (BOLD) fMRI, where an edge (called hyper-edge) can be connected to more than two nodes, are effective biomarkers for MCI classification. Although BOLD fMRI is a high temporal resolution fMRI approach to assess alterations in brain networks, it cannot pinpoint to a single correlation of neuronal activity since BOLD signals are composite. In contrast, arterial spin labeling (ASL) is a lower temporal resolution fMRI technique for measuring cerebral blood flow (CBF) that can provide quantitative, direct brain network physiology measurements...
November 13, 2018: Medical Image Analysis
J Lindemeyer, A-M Oros-Peusquens, N J Shah
In Magnetic Resonance Imaging, mapping of the static magnetic field and the magnetic susceptibility is based on multidimensional phase measurements. Phase data are ambiguous and have to be unwrapped to their true range in order to exhibit a correct representation of underlying features. High-resolution imaging at ultra-high fields, where susceptibility and phase contrast are natural tools, can generate large datasets, which tend to dramatically increase computing time demands for spatial unwrapping algorithms...
November 13, 2018: Medical Image Analysis
Yiyuan Zhao, Srijata Chakravorti, Robert F Labadie, Benoit M Dawant, Jack H Noble
Cochlear implants (CIs) are neural prosthetics that provide a sense of sound to people who experience severe to profound hearing loss. Recent studies have demonstrated a correlation between hearing outcomes and intra-cochlear locations of CI electrodes. Our group has been conducting investigations on this correlation and has been developing an image-guided cochlear implant programming (IGCIP) system to program CI devices to improve hearing outcomes. One crucial step that has not been automated in IGCIP is the localization of CI electrodes in clinical CTs...
November 13, 2018: Medical Image Analysis
Timo Roine, Ben Jeurissen, Daniele Perrone, Jan Aelterman, Wilfried Philips, Jan Sijbers, Alexander Leemans
Diffusion-weighted magnetic resonance imaging can be used to non-invasively probe the brain microstructure. In addition, recent advances have enabled the identification of complex fiber configurations present in most of the white matter. This has improved the investigation of structural connectivity with tractography methods. Whole-brain structural connectivity networks, or connectomes, are reconstructed by parcellating the gray matter and performing tractography to determine connectivity between these regions...
October 26, 2018: Medical Image Analysis
Mohammad I Daoud, Abdel-Latif Alshalalfah, Otmane Ait Mohamed, Rami Alazrai
Three-dimensional (3D) motorized curvilinear ultrasound probes provide an effective, low-cost tool to guide needle interventions, but localizing and tracking the needle in 3D ultrasound volumes is often challenging. In this study, a new method is introduced to localize and track the needle using 3D motorized curvilinear ultrasound probes. In particular, a low-cost camera mounted on the probe is employed to estimate the needle axis. The camera-estimated axis is used to identify a volume of interest (VOI) in the ultrasound volume that enables high needle visibility...
October 3, 2018: Medical Image Analysis
Guy Nir, Soheil Hor, Davood Karimi, Ladan Fazli, Brian F Skinnider, Peyman Tavassoli, Dmitry Turbin, Carlos F Villamil, Gang Wang, R Storey Wilson, Kenneth A Iczkowski, M Scott Lucia, Peter C Black, Purang Abolmaesumi, S Larry Goldenberg, Septimiu E Salcudean
Prostate cancer (PCa) is a heterogeneous disease that is manifested in a diverse range of histologic patterns and its grading is therefore associated with an inter-observer variability among pathologists, which may lead to an under- or over-treatment of patients. In this work, we develop a computer aided diagnosis system for automatic grading of PCa in digitized histopathology images using supervised learning methods. Our pipeline comprises extraction of multi-scale features that include glandular, cellular, and image-based features...
September 24, 2018: Medical Image Analysis
Mathilde Giacalone, Pejman Rasti, Noelie Debs, Carole Frindel, Tae-Hee Cho, Emmanuel Grenier, David Rousseau
We address the medical image analysis issue of predicting the final lesion in stroke from early perfusion magnetic resonance imaging. The classical processing approach for the dynamical perfusion images consists in a temporal deconvolution to improve the temporal signals associated with each voxel before performing prediction. We demonstrate here the value of exploiting directly the raw perfusion data by encoding the local environment of each voxel as a spatio-temporal texture, with an observation scale larger than the voxel...
September 23, 2018: Medical Image Analysis
Juan Eugenio Iglesias, Marc Modat, Loïc Peter, Allison Stevens, Roberto Annunziata, Tom Vercauteren, Ed Lein, Bruce Fischl, Sebastien Ourselin
Nonlinear registration of 2D histological sections with corresponding slices of MRI data is a critical step of 3D histology reconstruction algorithms. This registration is difficult due to the large differences in image contrast and resolution, as well as the complex nonrigid deformations and artefacts produced when sectioning the sample and mounting it on the glass slide. It has been shown in brain MRI registration that better spatial alignment across modalities can be obtained by synthesising one modality from the other and then using intra-modality registration metrics, rather than by using information theory based metrics to solve the problem directly...
September 22, 2018: Medical Image Analysis
Yubing Tong, Jayaram K Udupa, Dewey Odhner, Caiyun Wu, Stephen J Schuster, Drew A Torigian
PURPOSE: The derivation of quantitative information from images in a clinically practical way continues to face a major hurdle because of image segmentation challenges. This paper presents a novel approach, called automatic anatomy recognition-disease quantification (AAR-DQ), for disease quantification (DQ) on positron emission tomography/computed tomography (PET/CT) images. This approach explores how to decouple DQ methods from explicit dependence on object (e.g., organ) delineation through the use of only object recognition results from our recently developed automatic anatomy recognition (AAR) method to quantify disease burden...
January 2019: Medical Image Analysis
Longwei Fang, Lichi Zhang, Dong Nie, Xiaohuan Cao, Islem Rekik, Seong-Whan Lee, Huiguang He, Dinggang Shen
Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atlases to the target image, and then propagate the labels from the labeled atlases to the unlabeled target image. However, the registration step involves non-rigid alignment, which is often time-consuming and might lack high accuracy. Alternatively, patch-based methods have shown promise in relaxing the demand for accurate registration, but they often require the use of hand-crafted features...
January 2019: Medical Image Analysis
Yuta Hiasa, Yoshito Otake, Rie Tanaka, Shigeru Sanada, Yoshinobu Sato
Dynamic chest radiography (2D x-ray video) is a low-dose and cost-effective functional imaging method with high temporal resolution. While the analysis of rib-cage motion has been shown to be effective for evaluating respiratory function, it has been limited to 2D. We aim at 3D rib-motion analysis for high temporal resolution while keeping the radiation dose at a level comparable to conventional examination. To achieve this, we developed a method for automatically recovering 3D rib motion based on 2D-3D registration of x-ray video and single-time-phase computed tomography...
January 2019: Medical Image Analysis
Fuchen Liu, Charles-André Cuenod, Isabelle Thomassin-Naggara, Stéphane Chemouny, Yves Rozenholc
Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarkers for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). SNR may be improved by averaging functional information in a large region of interest when it is functionally homogeneous. We propose a novel method for automatic segmentation of DCE image sequences into functionally homogeneous regions, called DCE-HiSET...
January 2019: Medical Image Analysis
Cristina Gallego-Ortiz, Anne L Martel
Nonmass-like enhancements are a common but diagnostically challenging finding in breast MRI. Nonmass-like lesions can be described as clusters of spatially and temporally inter-connected regions of enhancements, so they can be modeled as networks and their properties characterized via network-based connectivity. In this work, we represented nonmass lesions as graphs using a link formation energy model that favors linkages between regions of similar enhancement and closer spatial proximity. However, adding graph features to an existing computer-aided diagnosis (CAD) pipeline incurs an increase of feature space dimensionality, which poses additional challenges to traditional supervised machine learning techniques due to the inability to increase accordingly the number of training datasets...
January 2019: Medical Image Analysis
Naji Khosravan, Haydar Celik, Baris Turkbey, Elizabeth C Jones, Bradford Wood, Ulas Bagci
Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors or misdiagnose. In this regard, eye-trackers have been instrumental in understanding visual search processes of radiologists. However, most relevant studies in this aspect are not compatible with realistic radiology reading rooms. In this study, we aim to develop a paradigm shifting CAD system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings...
January 2019: Medical Image Analysis
Florian Dubost, Hieab Adams, Gerda Bortsova, M Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automated quantification of EPVS would greatly help to advance research into its etiology and its potential as a risk indicator of disease. We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI. We first segment the basal ganglia and subsequently apply a 3D convolutional regression network designed for small object detection within this region of interest...
January 2019: Medical Image Analysis
Jordina Torrents-Barrena, Gemma Piella, Narcís Masoller, Eduard Gratacós, Elisenda Eixarch, Mario Ceresa, Miguel Ángel González Ballester
Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus...
January 2019: Medical Image Analysis
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