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Medical Image Analysis

Esther Puyol-Antón, Matthew Sinclair, Bernhard Gerber, Mihaela Silvia Amzulescu, Hélène Langet, Mathieu De Craene, Paul Aljabar, Paolo Piro, Andrew P King
Cardiac motion atlases provide a space of reference in which the motions of a cohort of subjects can be directly compared. Motion atlases can be used to learn descriptors that are linked to different pathologies and which can subsequently be used for diagnosis. To date, all such atlases have been formed and applied using data from the same modality. In this work we propose a framework to build a multimodal cardiac motion atlas from 3D magnetic resonance (MR) and 3D ultrasound (US) data. Such an atlas will benefit from the complementary motion features derived from the two modalities, and furthermore, it could be applied in clinics to detect cardiovascular disease using US data alone...
June 13, 2017: Medical Image Analysis
Danilo Samuel Jodas, Aledir Silveira Pereira, João Manuel R S Tavares
Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and the identification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on the K-means algorithm and the mean roundness to identify the region corresponding to the potential lumen...
June 10, 2017: Medical Image Analysis
Andreas Schoob, Dennis Kundrat, Lüder A Kahrs, Tobias Ortmaier
Recent research has revealed that image-based methods can enhance accuracy and safety in laser microsurgery. In this study, non-rigid tracking using surgical stereo imaging and its application to laser ablation is discussed. A recently developed motion estimation framework based on piecewise affine deformation modeling is extended by a mesh refinement step and considering texture information. This compensates for tracking inaccuracies potentially caused by inconsistent feature matches or drift. To facilitate online application of the method, computational load is reduced by concurrent processing and affine-invariant fusion of tracking and refinement results...
June 8, 2017: Medical Image Analysis
Gonzalo Vegas-Sánchez-Ferrero, Maria J Ledesma-Carbayo, George R Washko, Raúl San José Estépar
Computerized tomography (CT) is a widely adopted modality for analyzing directly or indirectly functional, biological and morphological processes by means of the image characteristics. However, the potential utilization of the information obtained from CT images is often limited when considering the analysis of quantitative information involving different devices, acquisition protocols or reconstruction algorithms. Although CT scanners are calibrated as a part of the imaging workflow, the calibration is circumscribed to global reference values and does not circumvent problems that are inherent to the imaging modality...
June 7, 2017: Medical Image Analysis
James M Brown, Ewan Ross, Guillaume Desanti, Atif Saghir, Andy Clark, Chris Buckley, Andrew Filer, Amy Naylor, Ela Claridge
Rheumatoid arthritis (RA) is an autoimmune disease in which chronic inflammation of the synovial joints can lead to destruction of cartilage and bone. Pre-clinical studies attempt to uncover the underlying causes by emulating the disease in genetically different mouse strains and characterising the nature and severity of bone shape changes as indicators of pathology. This paper presents a fully automated method for obtaining quantitative measurements of bone destruction from volumetric micro-CT images of a mouse hind paw...
May 23, 2017: Medical Image Analysis
Yan Wang, Florent Seguro, Evan Kao, Yue Zhang, Farshid Faraji, Chengcheng Zhu, Henrik Haraldsson, Michael Hope, David Saloner, Jing Liu
Segmentation of the geometric morphology of abdominal aortic aneurysm is important for interventional planning. However, the segmentation of both the lumen and the outer wall of aneurysm in magnetic resonance (MR) image remains challenging. This study proposes a registration based segmentation methodology for efficiently segmenting MR images of abdominal aortic aneurysms. The proposed methodology first registers the contrast enhanced MR angiography (CE-MRA) and black-blood MR images, and then uses the Hough transform and geometric active contours to extract the vessel lumen by delineating the inner vessel wall directly from the CE-MRA...
May 19, 2017: Medical Image Analysis
Zhengxia Wang, Xiaofeng Zhu, Ehsan Adeli, Yingying Zhu, Feiping Nie, Brent Munsell, Guorong Wu
Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is based on similarities in voxel intensity values in the feature domain, which can then be used to propagate the known phenotype data (i.e., clinical scores and labels) from the training data to the testing data in the label domain. However, this type of graph is exclusively learned in the feature domain, and primarily due to outliers in the observed features, may not be optimal for label propagation in the label domain...
May 13, 2017: 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
Gopalkrishna Veni, Shireen Y Elhabian, Ross T Whitaker
A variety of medical image segmentation problems present significant technical challenges, including heterogeneous pixel intensities, noisy/ill-defined boundaries and irregular shapes with high variability. The strategy of estimating optimal segmentations within a statistical framework that combines image data with priors on anatomical structures promises to address some of these technical challenges. However, methods that rely on local optimization techniques and/or local shape penalties (e.g., smoothness) have been proven to be inadequate for many difficult segmentation problems...
April 29, 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
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