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Medical Image Analysis

Devis Peressutti, Matthew Sinclair, Wenjia Bai, Thomas Jackson, Jacobus Ruijsink, David Nordsletten, Liya Asner, Myrianthi Hadjicharalambous, Christopher A Rinaldi, Daniel Rueckert, Andrew P King
We present a framework for combining a cardiac motion atlas with non-motion data. The atlas represents cardiac cycle motion across a number of subjects in a common space based on rich motion descriptors capturing 3D displacement, velocity, strain and strain rate. The non-motion data are derived from a variety of sources such as imaging, electrocardiogram (ECG) and clinical reports. Once in the atlas space, we apply a novel supervised learning approach based on random projections and ensemble learning to learn the relationship between the atlas data and some desired clinical output...
October 11, 2016: Medical Image Analysis
Loïc Peter, Diana Mateus, Pierre Chatelain, Denis Declara, Noemi Schworm, Stefan Stangl, Gabriele Multhoff, Nassir Navab
The examination of biopsy samples plays a central role in the diagnosis and staging of numerous diseases, including most cancer types. However, because of the large size of the acquired images, the localization and quantification of diseased portions of a tissue is usually time-consuming, as pathologists must scroll through the whole slide to look for objects of interest which are often only scarcely distributed. In this work, we introduce an approach to facilitate the visual inspection of large digital histopathological slides...
October 5, 2016: Medical Image Analysis
Ruud Jg van Sloun, Libertario Demi, Arnoud W Postema, Jean Jmch de la Rosette, Hessel Wijkstra, Massimo Mischi
Prostate cancer (PCa) is the second-leading cause of cancer death in men; however, reliable tools for detection and localization are still lacking. Dynamic Contrast Enhanced UltraSound (DCE-US) is a diagnostic tool that is suitable for analysis of vascularization, by imaging an intravenously injected microbubble bolus. The localization of angiogenic vascularization associated with the development of tumors is of particular interest. Recently, methods for the analysis of the bolus convective dispersion process have shown promise to localize angiogenesis...
October 1, 2016: Medical Image Analysis
A L P Nunes, A Maciel, L T Cavazzola, M Walter
While improved visual realism is known to enhance training effectiveness in virtual surgery simulators, the advances on realistic rendering for these simulators is slower than similar simulations for man-made scenes. One of the main reasons for this is that in vivo data is hard to gather and process. In this paper, we propose the analysis of videolaparoscopy data to compute the Bidirectional Reflectance Distribution Function (BRDF) of living organs as an input to physically based rendering algorithms. From the interplay between light and organic matter recorded in video images, we propose the definition of a process capable of establishing the BRDF for inside-the-body organic surfaces...
September 29, 2016: Medical Image Analysis
Fan Zhang, Jingjing Kanik, Tommaso Mansi, Ingmar Voigt, Puneet Sharma, Razvan Ioan Ionasec, Lakshman Subrahmanyan, Ben A Lin, Lissa Sugeng, David Yuh, Dorin Comaniciu, James Duncan
Transesophageal echocardiography (TEE) is routinely used to provide important qualitative and quantitative information regarding mitral regurgitation. Contemporary planning of surgical mitral valve repair, however, still relies heavily upon subjective predictions based on experience and intuition. While patient-specific mitral valve modeling holds promise, its effectiveness is limited by assumptions that must be made about constitutive material properties. In this paper, we propose and develop a semi-automated framework that combines machine learning image analysis with geometrical and biomechanical models to build a patient-specific mitral valve representation that incorporates image-derived material properties...
September 27, 2016: Medical Image Analysis
Sieun Lee, Nicolas Charon, Benjamin Charlier, Karteek Popuri, Evgeniy Lebed, Marinko V Sarunic, Alain Trouvé, Mirza Faisal Beg
We propose a novel approach for quantitative shape variability analysis in retinal optical coherence tomography images using the functional shape (fshape) framework. The fshape framework uses surface geometry together with functional measures, such as retinal layer thickness defined on the layer surface, for registration across anatomical shapes. This is used to generate a population mean template of the geometry-function measures from each individual. Shape variability across multiple retinas can be measured by the geometrical deformation and functional residual between the template and each of the observations...
September 20, 2016: Medical Image Analysis
Chuyang Ye, Jerry L Prince
Diffusion magnetic resonance imaging (dMRI) can be used for noninvasive imaging of white matter tracts. Using fiber tracking, which propagates fiber streamlines according to fiber orientations (FOs) computed from dMRI, white matter tracts can be reconstructed for investigation of brain diseases and the brain connectome. Because of image noise, probabilistic tractography has been proposed to characterize uncertainties in FO estimation. Bootstrap provides a nonparametric approach to the estimation of FO uncertainties and residual bootstrap has been used for developing probabilistic tractography...
September 16, 2016: Medical Image Analysis
Lucas Royer, Alexandre Krupa, Guillaume Dardenne, Anthony Le Bras, Eric Marchand, Maud Marchal
In this paper, we present a real-time approach that allows tracking deformable structures in 3D ultrasound sequences. Our method consists in obtaining the target displacements by combining robust dense motion estimation and mechanical model simulation. We perform evaluation of our method through simulated data, phantom data, and real-data. Results demonstrate that this novel approach has the advantage of providing correct motion estimation regarding different ultrasound shortcomings including speckle noise, large shadows and ultrasound gain variation...
September 15, 2016: Medical Image Analysis
Xiaoke Cui, Takumi Washio, Tomoaki Chono, Hirotaka Baba, Jun-Ichi Okada, Seiryo Sugiura, Toshiaki Hisada
By tracking echocardiography images more accurately and stably, we can better assess myocardial functions. In this paper, we propose a new tracking method with deformable Regions of Interest (ROIs) aiming at rational pattern matching. For this purpose we defined multiple tracking points for an ROI and regarded these points as nodes in the Meshfree Method to interpolate displacement fields. To avoid unreasonable distortion of the ROI caused by noise and perturbation in echo images, we introduced a stabilization technique based on a nonlinear strain energy function...
September 15, 2016: Medical Image Analysis
David Bouget, Max Allan, Danail Stoyanov, Pierre Jannin
In recent years, tremendous progress has been made in surgical practice for example with Minimally Invasive Surgery (MIS). To overcome challenges coming from deported eye-to-hand manipulation, robotic and computer-assisted systems have been developed. Having real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy is a key ingredient for such systems. In this paper, we present a review of the literature dealing with vision-based and marker-less surgical tool detection...
September 13, 2016: Medical Image Analysis
Cheng Zhong, Ju Han, Alexander Borowsky, Bahram Parvin, Yunfu Wang, Hang Chang
Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical variations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data...
September 9, 2016: Medical Image Analysis
Jie Shi, Wen Zhang, Miao Tang, Richard J Caselli, Yalin Wang
Landmark curves were widely adopted in neuroimaging research for surface correspondence computation and quantified morphometry analysis. However, most of the landmark based morphometry studies only focused on landmark curve shape difference. Here we propose to compute a set of conformal invariant-based shape indices, which are associated with the landmark curve induced boundary lengths in the hyperbolic parameter domain. Such shape indices may be used to identify which surfaces are conformally equivalent and further quantitatively measure surface deformation...
September 6, 2016: Medical Image Analysis
Pedro Pedrosa Rebouças Filho, Paulo César Cortez, Antônio C da Silva Barros, Victor Hugo C Albuquerque, João Manuel R S Tavares
The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician...
September 5, 2016: Medical Image Analysis
Korsuk Sirinukunwattana, Josien P W Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J Matuszewski, Elia Bruni, Urko Sanchez, Anton Böhm, Olaf Ronneberger, Bassem Ben Cheikh, Daniel Racoceanu, Philipp Kainz, Michael Pfeiffer, Martin Urschler, David R J Snead, Nasir M Rajpoot
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem...
September 3, 2016: Medical Image Analysis
Amod Jog, Aaron Carass, Snehashis Roy, Dzung L Pham, Jerry L Prince
By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T2-weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported...
August 31, 2016: Medical Image Analysis
Pietro Gori, Olivier Colliot, Linda Marrakchi-Kacem, Yulia Worbe, Cyril Poupon, Andreas Hartmann, Nicholas Ayache, Stanley Durrleman
We present a Bayesian framework for atlas construction of multi-object shape complexes comprised of both surface and curve meshes. It is general and can be applied to any parametric deformation framework and to all shape models with which it is possible to define probability density functions (PDF). Here, both curve and surface meshes are modelled as Gaussian random varifolds, using a finite-dimensional approximation space on which PDFs can be defined. Using this framework, we can automatically estimate the parameters balancing data-terms and deformation regularity, which previously required user tuning...
August 30, 2016: Medical Image Analysis
Sergi Valverde, Arnau Oliver, Eloy Roura, Sandra González-Villà, Deborah Pareto, Joan C Vilanova, Lluís Ramió-Torrentà, Àlex Rovira, Xavier Lladó
Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions...
August 30, 2016: Medical Image Analysis
David Parker, Xueqing Liu, Qolamreza R Razlighi
Due to the nature of fMRI acquisition protocols, slices in the plane of acquisition are not acquired simultaneously or sequentially, and therefore are temporally misaligned with each other. Slice timing correction (STC) is a critical preprocessing step that corrects for this temporal misalignment. Interpolation-based STC is implemented in all major fMRI processing software packages. To date, little effort has gone towards assessing the optimal method of STC. Delineating the benefits of STC can be challenging because of its slice-dependent gain as well as its interaction with other fMRI artifacts...
August 24, 2016: Medical Image Analysis
Ian J Gerard, Marta Kersten-Oertel, Kevin Petrecca, Denis Sirhan, Jeffery A Hall, D Louis Collins
PURPOSE: Neuronavigation based on preoperative imaging data is a ubiquitous tool for image guidance in neurosurgery. However, it is rendered unreliable when brain shift invalidates the patient-to-image registration. Many investigators have tried to explain, quantify, and compensate for this phenomenon to allow extended use of neuronavigation systems for the duration of surgery. The purpose of this paper is to present an overview of the work that has been done investigating brain shift...
August 24, 2016: Medical Image Analysis
Vigneshwaran Subbaraju, Mahanand Belathur Suresh, Suresh Sundaram, Sundararajan Narasimhan
This paper presents a new approach for detecting major differences in brain activities between Autism Spectrum Disorder (ASD) patients and neurotypical subjects using the resting state fMRI. Further the method also extracts discriminative features for an accurate diagnosis of ASD. The proposed approach determines a spatial filter that projects the covariance matrices of the Blood Oxygen Level Dependent (BOLD) time-series signals from both the ASD patients and neurotypical subjects in orthogonal directions such that they are highly separable...
August 23, 2016: Medical Image Analysis
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