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Medical Image Analysis

Xiaohuan Cao, Jianhua Yang, Yaozong Gao, Yanrong Guo, Guorong Wu, Dinggang Shen
In prostate cancer radiotherapy, computed tomography (CT) is widely used for dose planning purposes. However, because CT has low soft tissue contrast, it makes manual contouring difficult for major pelvic organs. In contrast, magnetic resonance imaging (MRI) provides high soft tissue contrast, which makes it ideal for accurate manual contouring. Therefore, the contouring accuracy on CT can be significantly improved if the contours in MRI can be mapped to CT domain by registering MRI with CT of the same subject, which would eventually lead to high treatment efficacy...
May 13, 2017: Medical Image Analysis
Aïcha BenTaieb, Hector Li-Chang, David Huntsman, Ghassan Hamarneh
Accurate subtyping of ovarian carcinomas is an increasingly critical and often challenging diagnostic process. This work focuses on the development of an automatic classification model for ovarian carcinoma subtyping. Specifically, we present a novel clinically inspired contextual model for histopathology image subtyping of ovarian carcinomas. A whole slide image is modelled using a collection of tissue patches extracted at multiple magnifications. An efficient and effective feature learning strategy is used for feature representation of a tissue patch...
May 9, 2017: Medical Image Analysis
Qi Dou, Lequan Yu, Hao Chen, Yueming Jin, Xin Yang, Jing Qin, Pheng-Ann Heng
While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN...
May 8, 2017: Medical Image Analysis
Mohammad Saleh Miri, Michael D Abràmoff, Young H Kwon, Milan Sonka, Mona K Garvin
Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Measuring this structural parameter requires identification of BMO locations within spectral domain-optical coherence tomography (SD-OCT) volumes. While most automated approaches for segmentation of the BMO either segment the 2D projection of BMO points or identify BMO points in individual B-scans, in this work, we propose a machine-learning graph-based approach for true 3D segmentation of BMO from glaucomatous SD-OCT volumes...
May 6, 2017: Medical Image Analysis
Benjamin Gutierrez-Becker, Diana Mateus, Loic Peter, Nassir Navab
In this paper, we address the multimodal registration problem from a novel perspective, aiming to predict the transformation aligning images directly from their visual appearance. We formulate the prediction as a supervised regression task, with joint image descriptors as input and the output are the parameters of the transformation that guide the moving image towards alignment. We model the joint local appearance with context aware descriptors that capture both local and global cues simultaneously in the two modalities, while the regression function is based on the gradient boosted trees method capable of handling the very large contextual feature space...
May 6, 2017: Medical Image Analysis
Lars Forsberg, Sigurdur Sigurdsson, Jesper Fredriksson, Asdis Egilsdottir, Bryndis Oskarsdottir, Olafur Kjartansson, Mark A van Buchem, Lenore J Launer, Vilmundur Gudnason, Alex Zijdenbos
Quantitative analyses of brain structures from Magnetic Resonance (MR) image data are often performed using automatic segmentation algorithms. Many of these algorithms rely on templates and atlases in a common coordinate space. Most freely available brain atlases are generated from relatively young individuals and not always derived from well-defined cohort studies. In this paper, we introduce a publicly available multi-spectral template with corresponding tissue probability atlases and regional atlases, optimised to use in studies of ageing cohorts (mean age 75 ± 5 years)...
May 6, 2017: Medical Image Analysis
Hua Ma, Ayla Hoogendoorn, Evelyn Regar, Wiro J Niessen, Theo van Walsum
Percutaneous coronary intervention is a minimally invasive procedure that is usually performed under image guidance using X-ray angiograms in which coronary arteries are opacified with contrast agent. In X-ray images, 3D objects are projected on a 2D plane, generating semi-transparent layers that overlap each other. The overlapping of structures makes robust automatic information processing of the X-ray images, such as vessel extraction which is highly relevant to support smart image guidance, challenging. In this paper, we propose an automatic online layer separation approach that robustly separates interventional X-ray angiograms into three layers: a breathing layer, a quasi-static layer and a vessel layer that contains information of coronary arteries and medical instruments...
May 5, 2017: Medical Image Analysis
Sila Kurugol, Moti Freiman, Onur Afacan, Liran Domachevsky, Jeannette M Perez-Rossello, Michael J Callahan, Simon K Warfield
Quantitative body DW-MRI can detect abdominal abnormalities as well as monitor response-to-therapy for applications including cancer and inflammatory bowel disease with increased accuracy. Parameter estimates are obtained by fitting a forward model of DW-MRI signal decay to the observed data acquired with several b-values. The DW-MRI signal decay models typically used do not account for respiratory, cardiac and peristaltic motion, however, which may deteriorate the accuracy and robustness of parameter estimates...
May 3, 2017: Medical Image Analysis
Gwenolé Quellec, Katia Charrière, Yassine Boudi, Béatrice Cochener, Mathieu Lamard
Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predictions...
April 28, 2017: Medical Image Analysis
Daniel Moyer, Boris A Gutman, Joshua Faskowitz, Neda Jahanshad, Paul M Thompson
We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each streamline curve in a tractography as an observed event in connectome space, here the product space of the gray matter/white matter interfaces. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden, we develop a fast parameter estimation method by pre-computing associated Legendre products of the data, leveraging properties of the spherical heat kernel...
April 28, 2017: Medical Image Analysis
Enzo Ferrante, Nikos Paragios
During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art and enabled the development of novel medical procedures. A particular type of image registration problem, known as slice-to-volume registration, played a fundamental role in areas like image guided surgeries and volumetric image reconstruction. However, to date, and despite the extensive literature available on this topic, no survey has been written to discuss this challenging problem...
April 28, 2017: Medical Image Analysis
Lianghao Han, Hua Dong, Jamie R McClelland, Liangxiu Han, David J Hawkes, Dean C Barratt
This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1...
April 19, 2017: 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
Chantal M W Tax, Carl-Fredrik Westin, Tom Dela Haije, Andrea Fuster, Max A Viergever, Evan Calabrese, Luc Florack, Alexander Leemans
The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as "pages of a book" has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of "path neighborhoods" reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis...
April 4, 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
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