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Medical Image Analysis

Rahil Shahzad, Qian Tao, Oleh Dzyubachyk, Marius Staring, Boudewijn P F Lelieveldt, Rob J van der Geest
With an increasing number of large-scale population-based cardiac magnetic resonance (CMR) imaging studies being conducted nowadays, there comes the mammoth task of image annotation and image analysis. Such population-based studies would greatly benefit from automated pipelines, with an efficient CMR image analysis workflow. The purpose of this work is to investigate the feasibility of using a fully-automatic pipeline to segment the left ventricular endocardium and epicardium simultaneously on two orthogonal (vertical and horizontal) long-axis cardiac cine MRI scans...
April 13, 2017: Medical Image Analysis
Li Kuo Tan, Yih Miin Liew, Einly Lim, Robert A McLaughlin
Automated left ventricular (LV) segmentation is crucial for efficient quantification of cardiac function and morphology to aid subsequent management of cardiac pathologies. In this paper, we parameterize the complete (all short axis slices and phases) LV segmentation task in terms of the radial distances between the LV centerpoint and the endo- and epicardial contours in polar space. We then utilize convolutional neural network regression to infer these parameters. Utilizing parameter regression, as opposed to conventional pixel classification, allows the network to inherently reflect domain-specific physical constraints...
April 12, 2017: Medical Image Analysis
John A Onofrey, Lawrence H Staib, Saradwata Sarkar, Rajesh Venkataraman, Cayce B Nawaf, Preston C Sprenkle, Xenophon Papademetris
Accurate and robust non-rigid registration of pre-procedure magnetic resonance (MR) imaging to intra-procedure trans-rectal ultrasound (TRUS) is critical for image-guided biopsies of prostate cancer. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. TRUS-guided biopsy is the current clinical standard for prostate cancer diagnosis and assessment. State-of-the-art, clinical MR-TRUS image fusion relies upon semi-automated segmentations of the prostate in both the MR and the TRUS images to perform non-rigid surface-based registration of the gland...
April 12, 2017: Medical Image Analysis
James Fishbaugh, Stanley Durrleman, Marcel Prastawa, Guido Gerig
Many problems in medicine are inherently dynamic processes which include the aspect of change over time, such as childhood development, aging, and disease progression. From medical images, numerous geometric structures can be extracted with various representations, such as landmarks, point clouds, curves, and surfaces. Different sources of geometry may characterize different aspects of the anatomy, such as fiber tracts from DTI and subcortical shapes from structural MRI, and therefore require a modeling scheme which can include various shape representations in any combination...
April 5, 2017: Medical Image Analysis
Andreas Nagler, Cristóbal Bertoglio, Christian T Stoeck, Sebastian Kozerke, Wolfgang A Wall
We propose an estimation scheme for local fiber bundle direction in the left ventricle directly from gray values of arbitrarily spaced cardiac diffusion weighted images (DWI). The approach is based on a parametric and space-dependent mathematical representation of the myocardial fiber bundle orientation and hence the diffusion tensor (DT) for the ventricular geometry. By solving a nonlinear inverse problem derived from a maximum likelihood estimator, the degrees of freedom of the fiber and DT model can be estimated from the measured gray values of the DWIs...
April 4, 2017: Medical Image Analysis
Ken'ichi Karasawa, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Chengwen Chu, Guoyan Zheng, Daniel Rueckert, Kensaku Mori
Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape...
March 31, 2017: Medical Image Analysis
Helen Schomburg, Thorsten Hohage
Fiber tractography based on Diffusion MRI measurements is a valuable tool for the detection and visual representation of neural pathways in vivo. We present a novel fiber orientation distribution function (ODF) based streamline tractography approach which incorporates information of neighboring regions derived from a Bayesian model. In each iteration step, the proposed algorithm defines a set of candidate fiber fragments continuing the already tracked path and assigns an a-posteriori probability. We compute the posterior as the normalized product of a likelihood function based on the given ODF-field and a prior distribution representing anatomical plausibility of a candidate fiber fragment with respect to tract curvature derived from the previously tracked fiber path by an extrapolation strategy...
March 24, 2017: Medical Image Analysis
Ramón Casero, Urszula Siedlecka, Elizabeth S Jones, Lena Gruscheski, Matthew Gibb, Jürgen E Schneider, Peter Kohl, Vicente Grau
Traditional histology is the gold standard for tissue studies, but it is intrinsically reliant on two-dimensional (2D) images. Study of volumetric tissue samples such as whole hearts produces a stack of misaligned and distorted 2D images that need to be reconstructed to recover a congruent volume with the original sample's shape. In this paper, we develop a mathematical framework called Transformation Diffusion (TD) for stack alignment refinement as a solution to the heat diffusion equation. This general framework does not require contour segmentation, is independent of the registration method used, and is trivially parallelizable...
March 23, 2017: Medical Image Analysis
Alessandro Vandini, Ben Glocker, Mohamad Hamady, Guang-Zhong Yang
Robust tracking of interventional tools, such as guidewires and catheters, in X-ray fluoroscopic video sequences has a wide range of clinical applications for endovascular procedures. Thus far, the tracking is usually achieved by finding the optimal displacement of the control points of a spline, which models the guidewire, between consecutive frames. The displacement of the control points is typically driven by a data term and smoothed by a regularization term. In the presence of large deformation and changes in length of the tool, the current tracking methods may fail to recover the guidewire motion...
March 15, 2017: Medical Image Analysis
Richard Rios, Renaud De Crevoisier, Juan D Ospina, Frederic Commandeur, Caroline Lafond, Antoine Simon, Pascal Haigron, Jairo Espinosa, Oscar Acosta
In radiotherapy for prostate cancer irradiation of neighboring organs at risk may lead to undesirable side-effects. Given this setting, the bladder presents the largest inter-fraction shape variations hampering the computation of the actual delivered dose vs. planned dose. This paper proposes a population model, based on longitudinal data, able to estimate the probability of bladder presence during treatment, using only the planning computed tomography (CT) scan as input information. As in previously-proposed principal component analysis (PCA) population-based models, we have used the data to obtain the dominant eigenmodes that describe bladder geometric variations between fractions...
March 8, 2017: Medical Image Analysis
Sunhua Wan, Hsiang-Chieh Lee, Xiaolei Huang, Ting Xu, Tao Xu, Xianxu Zeng, Zhan Zhang, Yuri Sheikine, James L Connolly, James G Fujimoto, Chao Zhou
This paper proposes a texture analysis technique that can effectively classify different types of human breast tissue imaged by Optical Coherence Microscopy (OCM). OCM is an emerging imaging modality for rapid tissue screening and has the potential to provide high resolution microscopic images that approach those of histology. OCM images, acquired without tissue staining, however, pose unique challenges to image analysis and pattern classification. We examined multiple types of texture features and found Local Binary Pattern (LBP) features to perform better in classifying tissues imaged by OCM...
March 8, 2017: Medical Image Analysis
Rodrigo Rojas-Moraleda, Wei Xiong, Niels Halama, Katja Breitkopf-Heinlein, Steven Dooley, Luis Salinas, Dieter W Heermann, Nektarios A Valous
The segmentation of cell nuclei is an important step towards the automated analysis of histological images. The presence of a large number of nuclei in whole-slide images necessitates methods that are computationally tractable in addition to being effective. In this work, a method is developed for the robust segmentation of cell nuclei in histological images based on the principles of persistent homology. More specifically, an abstract simplicial homology approach for image segmentation is established. Essentially, the approach deals with the persistence of disconnected sets in the image, thus identifying salient regions that express patterns of persistence...
March 6, 2017: Medical Image Analysis
Yang Song, Qing Li, Fan Zhang, Heng Huang, Dagan Feng, Yue Wang, Mei Chen, Weidong Cai
In aging research, morphological age of tissue helps to characterize the effects of aging on different individuals. While currently manual evaluations are used to estimate morphological ages under microscopy, such operation is difficult and subjective due to the complex visual characteristics of tissue images. In this paper, we propose an automated method to quantify morphological ages of tissues from microscopy images. We design a new sparse representation method, namely dual discriminative local coding (DDLC), that classifies the tissue images into different chronological ages...
February 27, 2017: Medical Image Analysis
Carole H Sudre, M Jorge Cardoso, Sebastien Ourselin
Although white matter hyperintensities evolve in the course of ageing, few solutions exist to consider the lesion segmentation problem longitudinally. Based on an existing automatic lesion segmentation algorithm, a longitudinal extension is proposed. For evaluation purposes, a longitudinal lesion simulator is created allowing for the comparison between the longitudinal and the cross-sectional version in various situations of lesion load progression. Finally, applied to clinical data, the proposed framework demonstrates an increased robustness compared to available cross-sectional methods and findings are aligned with previously reported clinical patterns...
February 24, 2017: Medical Image Analysis
Changfa Shi, Yuanzhi Cheng, Jinke Wang, Yadong Wang, Kensaku Mori, Shinichi Tamura
One major limiting factor that prevents the accurate delineation of human organs has been the presence of severe pathology and pathology affecting organ borders. Overcoming these limitations is exactly what we are concerned in this study. We propose an automatic method for accurate and robust pathological organ segmentation from CT images. The method is grounded in the active shape model (ASM) framework. It leverages techniques from low-rank and sparse decomposition (LRSD) theory to robustly recover a subspace from grossly corrupted data...
February 22, 2017: Medical Image Analysis
François Varray, Iulia Mirea, Max Langer, Françoise Peyrin, Laurent Fanton, Isabelle E Magnin
This paper presents a methodology to access the 3D local myocyte arrangements in fresh human post-mortem heart samples. We investigated the cardiac micro-structure at a high and isotropic resolution of 3.5 µm in three dimensions using X-ray phase micro-tomography at the European Synchrotron Radiation Facility. We then processed the reconstructed volumes to extract the 3D local orientation of the myocytes using a multi-scale approach with no segmentation. We created a simplified 3D model of tissue sample made of simulated myocytes with known size and orientations, to evaluate our orientation extraction method...
February 20, 2017: Medical Image Analysis
Matthias Wilms, Heinz Handels, Jan Ehrhardt
Statistical shape models learned from a population of previously observed training shapes are nowadays widely used in medical image analysis to aid segmentation or classification. However, providing an appropriate and representative training population of preferably manual segmentations is typically either very labor-intensive or even impossible. Therefore, statistical shape models in practice frequently suffer from the high-dimension-low-sample-size (HDLSS) problem resulting in models with insufficient expressiveness...
February 17, 2017: Medical Image Analysis
Florian Bernard, Luis Salamanca, Johan Thunberg, Alexander Tack, Dennis Jentsch, Hans Lamecker, Stefan Zachow, Frank Hertel, Jorge Goncalves, Peter Gemmar
The reconstruction of an object's shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process of aligning intra-operative navigation and preoperative planning data. In such scenarios, one usually has to deal with sparse data, which significantly aggravates the problem of reconstruction. However, medical applications often provide contextual information about the 3D point data that allow to incorporate prior knowledge about the shape that is to be reconstructed...
February 14, 2017: Medical Image Analysis
Jinglei Lv, Binbin Lin, Qingyang Li, Wei Zhang, Yu Zhao, Xi Jiang, Lei Guo, Junwei Han, Xintao Hu, Christine Guo, Jieping Ye, Tianming Liu
Task functional magnetic resonance imaging (fMRI) has been widely employed for brain activation detection and brain network analysis. Modeling rich information from spatially-organized collection of fMRI time series is challenging because of the intrinsic complexity. Hypothesis-driven methods, such as the general linear model (GLM), which regress exterior stimulus from voxel-wise functional brain activity, are limited due to overlooking the complexity of brain activities and the diversity of concurrent brain networks...
May 2017: Medical Image Analysis
Yasser Ghanbari, Luke Bloy, Birkan Tunc, Varsha Shankar, Timothy P L Roberts, J Christopher Edgar, Robert T Schultz, Ragini Verma
Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain...
May 2017: Medical Image Analysis
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