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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
Shulong Li, Ning Yang, Bin Li, Zhiguo Zhou, Hongxia Hao, Michael R Folkert, Puneeth Iyengar, Kenneth Westover, Hak Choy, Robert Timmerman, Steve Jiang, Jing Wang
We developed a kernelled support tensor machine (KSTM)-based model with tumor tensors derived from pre-treatment PET and CT imaging as input to predict distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). The patient cohort included 110 early stage NSCLC patients treated with SBRT, 25 of whom experienced failure at distant sites. Three-dimensional tumor tensors were constructed and used as input for the KSTM-based classifier. A KSTM iterative algorithm with a convergent proof was developed to train the weight vectors for every mode of the tensor for the classifier...
September 15, 2018: Medical Image Analysis
Zhi Chen, Michal Pazdernik, Honghai Zhang, Andreas Wahle, Zhihui Guo, Helena Bedanova, Josef Kautzner, Vojtech Melenovsky, Tomas Kovarnik, Milan Sonka
Cardiac allograft vasculopathy (CAV) accounts for about 30% of all heart-transplant (HTx) patient deaths. For patients at high risk for CAV complications after HTx, therapy must be initiated early to be effective. Therefore, new phenotyping approaches are needed to identify such HTx patients at the earliest possible time. Coronary optical coherence tomography (OCT) images were acquired from 50 HTx patients 1 and 12 months after HTx. Quantitative analysis of coronary wall morphology used LOGISMOS segmentation strategy to simultaneously identify three wall-layer surfaces for the entire pullback length in 3D: luminal, outer intimal, and outer medial surfaces...
September 14, 2018: Medical Image Analysis
Chenchu Xu, Lei Xu, Zhifan Gao, Shen Zhao, Heye Zhang, Yanping Zhang, Xiuquan Du, Shu Zhao, Dhanjoo Ghista, Huafeng Liu, Shuo Li
Changes in mechanical properties of myocardium caused by a infarction can lead to kinematic abnormalities. This phenomenon has inspired us to develop this work for delineation of myocardial infarction area directly from non-contrast agents cardiac MR imaging sequences. The main contribution of this work is to develop a new joint motion feature learning architecture to efficiently establish direct correspondences between motion features and tissue properties. This architecture consists of three seamless connected function layers: the heart localization layers can automatically crop the region of interest (ROI) sequences involving the left ventricle from the cardiac MR imaging sequences; the motion feature extraction layers, using long short-term memory-recurrent neural networks, a) builds patch-based motion features through local intensity changes between fixed-size patch sequences (cropped from image sequences), and b) uses optical flow techniques to build image-based features through global intensity changes between adjacent images to describe the motion of each pixel; the fully connected discriminative layers can combine two types of motion features together in each pixel and then build the correspondences between motion features and tissue identities (that is, infarct or not) in each pixel...
September 6, 2018: Medical Image Analysis
Laurent Lejeune, Jan Grossrieder, Raphael Sznitman
Recent machine learning strategies for segmentation tasks have shown great ability when trained on large pixel-wise annotated image datasets. It remains a major challenge however to aggregate such datasets, as the time and monetary cost associated with collecting extensive annotations is extremely high. This is particularly the case for generating precise pixel-wise annotations in video and volumetric image data. To this end, this work presents a novel framework to produce pixel-wise segmentations using minimal supervision...
August 29, 2018: Medical Image Analysis
L Joskowicz, D Cohen, N Caplan, J Sosna
PURPOSE: Segmentations produced manually by experts or by algorithms are subject to variability, as they depend on many factors, e.g., the structure of interest, the resolution, contrast and quality of the images, and the expert experience or the algorithmic method. To properly assess the quality of these segmentations, it is thus essential to quantify their variability. However, obtaining reference variability ground truth requires several observers to manually delineate structures, which is time-consuming and impractical...
August 26, 2018: Medical Image Analysis
Zhongyi Han, Benzheng Wei, Ashley Mercado, Stephanie Leung, Shuo Li
Spinal clinicians still rely on laborious workloads to conduct comprehensive assessments of multiple spinal structures in MRIs, in order to detect abnormalities and discover possible pathological factors. The objective of this work is to perform automated segmentation and classification (i.e., normal and abnormal) of intervertebral discs, vertebrae, and neural foramen in MRIs in one shot, which is called semantic segmentation that is extremely urgent to assist spinal clinicians in diagnosing neural foraminal stenosis, disc degeneration, and vertebral deformity as well as discovering possible pathological factors...
August 25, 2018: Medical Image Analysis
Rashed Karim, Lauren-Emma Blake, Jiro Inoue, Qian Tao, Shuman Jia, R James Housden, Pranav Bhagirath, Jean-Luc Duval, Marta Varela, Jonathan Behar, Loïc Cadour, Rob J van der Geest, Hubert Cochet, Maria Drangova, Maxime Sermesant, Reza Razavi, Oleg Aslanidi, Ronak Rajani, Kawal Rhode
Structural changes to the wall of the left atrium are known to occur with conditions that predispose to Atrial fibrillation. Imaging studies have demonstrated that these changes may be detected non-invasively. An important indicator of this structural change is the wall's thickness. Present studies have commonly measured the wall thickness at few discrete locations. Dense measurements with computer algorithms may be possible on cardiac scans of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The task is challenging as the atrial wall is a thin tissue and the imaging resolution is a limiting factor...
August 24, 2018: Medical Image Analysis
M Genet, C T Stoeck, C von Deuster, L C Lee, S Kozerke
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 represents a regularization with strong mechanical basis, especially suited for cardiac image analysis. We describe the consistent linearization and discretization of the regularized image registration problem, in the framework of the finite elements method...
August 22, 2018: Medical Image Analysis
Chen Chen, Lei He, Hongsheng Li, Junzhou Huang
In this paper, we propose a novel algorithm for analysis-based sparsity reconstruction. It can solve the generalized problem by structured sparsity regularization with an orthogonal basis and total variation (TV) regularization. The proposed algorithm is based on the iterative reweighted least squares (IRLS) framework, and is accelerated by the preconditioned conjugate gradient method. The proposed method is motivated by that, the Hessian matrix for many applications is diagonally dominant. The convergence rate of the proposed algorithm is empirically shown to be almost the same as that of the traditional IRLS algorithms, that is, linear convergence...
October 2018: Medical Image Analysis
Mariana Bustamante, Vikas Gupta, Daniel Forsberg, Carl-Johan Carlhäll, Jan Engvall, Tino Ebbers
Four-dimensional (4D) flow magnetic resonance imaging (4D Flow MRI) enables acquisition of time-resolved three-directional velocity data in the entire heart and all major thoracic vessels. The segmentation of these tissues is typically performed using semi-automatic methods. Some of which primarily rely on the velocity data and result in a segmentation of the vessels only during the systolic phases. Other methods, mostly applied on the heart, rely on separately acquired balanced Steady State Free Precession (b-SSFP) MR images, after which the segmentations are superimposed on the 4D Flow MRI...
October 2018: Medical Image Analysis
Yanna Cruz Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Simon Stute, Maria Ribeiro, Clovis Tauber
To analyze dynamic positron emission tomography (PET) images, various generic multivariate data analysis techniques have been considered in the literature, such as principal component analysis (PCA), independent component analysis (ICA), factor analysis and nonnegative matrix factorization (NMF). Nevertheless, these conventional approaches neglect any possible nonlinear variations in the time activity curves describing the kinetic behavior of tissues with specific binding, which limits their ability to recover a reliable, understandable and interpretable description of the data...
October 2018: Medical Image Analysis
Ken C L Wong, Tanveer Syeda-Mahmood, Mehdi Moradi
Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential. Although transfer learning with ImageNet pre-trained classification models can alleviate the problem, constrained image sizes and model complexities can lead to unnecessary increase in computational cost and decrease in performance. As many common morphological features are usually shared by different classification tasks of an organ, it is greatly beneficial if we can extract such features to improve classification with limited samples...
October 2018: Medical Image Analysis
Bruno Paun, Bart Bijnens, Andrew C Cook, Timothy J Mohun, Constantine Butakoff
During embryogenesis, a mammalian heart develops from a simple tubular shape into a complex 4-chamber organ, going through four distinct phases: early primitive tubular heart, emergence of trabeculations, trabecular remodeling and development of the compact myocardium. In this paper we propose a framework for standardized and subject-independent 3D regional myocardial complexity analysis, applied to analysis of the development of the mouse left ventricle. We propose a standardized subdivision of the myocardium into 3D overlapping regions (in our case 361) and a novel visualization of myocardial complexity, whereupon we: 1) extend the fractal dimension, commonly applied to image slices, to 3D and 2) use volume occupied by the trabeculations in each region together with their surface area, in order to quantify myocardial complexity...
October 2018: Medical Image Analysis
Jiri Chmelik, Roman Jakubicek, Petr Walek, Jiri Jan, Petr Ourednicek, Lukas Lambert, Elena Amadori, Giampaolo Gavelli
This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesions are ill-defined and consequently it is difficult to find relevant image features that would enable detection and classification of lesions by classical methods of texture and shape analysis, the problem is solved by automatic feature extraction provided by a deep Convolutional Neural Network (CNN)...
October 2018: Medical Image Analysis
Heran Yang, Jian Sun, Huibin Li, Lisheng Wang, Zongben Xu
Multi-atlas segmentation approach is one of the most widely-used image segmentation techniques in biomedical applications. There are two major challenges in this category of methods, i.e., atlas selection and label fusion. In this paper, we propose a novel multi-atlas segmentation method that formulates multi-atlas segmentation in a deep learning framework for better solving these challenges. The proposed method, dubbed deep fusion net (DFN), is a deep architecture that integrates a feature extraction subnet and a non-local patch-based label fusion (NL-PLF) subnet in a single network...
October 2018: Medical Image Analysis
Jing Xia, Fan Wang, Yu Meng, Zhengwang Wu, Li Wang, Weili Lin, Caiming Zhang, Dinggang Shen, Gang Li
The cortical surface of the human brain expands dynamically and regionally heterogeneously during the first postnatal year. As all primary and secondary cortical folds as well as many tertiary cortical folds are well established at term birth, the cortical surface area expansion during this stage is largely driven by the increase of surface area in two orthogonal orientations in the tangent plane: 1) the expansion parallel to the folding orientation (i.e., increasing the lengths of folds) and 2) the expansion perpendicular to the folding orientation (i...
October 2018: Medical Image Analysis
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