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Medical Image Analysis

Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrero, Ben Glocker, Daniel Rueckert
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes can represent individuals within a potentially large population (patients or healthy controls) accompanied by a set of features, while the graph edges incorporate associations between subjects in an intuitive manner. This representation allows to incorporate the wealth of imaging and non-imaging information as well as individual subject features simultaneously in disease classification tasks...
June 2, 2018: Medical Image Analysis
Emran Mohammad Abu Anas, Parvin Mousavi, Purang Abolmaesumi
Targeted prostate biopsy, incorporating multi-parametric magnetic resonance imaging (mp-MRI) and its registration with ultrasound, is currently the state-of-the-art in prostate cancer diagnosis. The registration process in most targeted biopsy systems today relies heavily on accurate segmentation of ultrasound images. Automatic or semi-automatic segmentation is typically performed offline prior to the start of the biopsy procedure. In this paper, we present a deep neural network based real-time prostate segmentation technique during the biopsy procedure, hence paving the way for dynamic registration of mp-MRI and ultrasound data...
June 1, 2018: Medical Image Analysis
Evan Schwab, René Vidal, Nicolas Charon
Diffusion MRI (dMRI) provides the ability to reconstruct neuronal fibers in the brain, in vivo, by measuring water diffusion along angular gradient directions in q-space. High angular resolution diffusion imaging (HARDI) can produce better estimates of fiber orientation than the popularly used diffusion tensor imaging, but the high number of samples needed to estimate diffusivity requires longer patient scan times. To accelerate dMRI, compressed sensing (CS) has been utilized by exploiting a sparse dictionary representation of the data, discovered through sparse coding...
May 24, 2018: Medical Image Analysis
Davis M Vigneault, Weidi Xie, Carolyn Y Ho, David A Bluemke, J Alison Noble
Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): A novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation...
May 22, 2018: Medical Image Analysis
D C T Nguyen, S Benameur, M Mignotte, F Lavoie
X-ray image segmentation is an important and crucial step for three-dimensional (3D) bone reconstruction whose final goal remains to increase effectiveness of computer-aided diagnosis, surgery and treatment plannings. However, this segmentation task is rather challenging, particularly when dealing with complicated human structures in the lower limb such as the patella, talus and pelvis. In this work, we present a multi-atlas fusion framework for the automatic segmentation of these complex bone regions from a single X-ray view...
May 18, 2018: Medical Image Analysis
Hongbo Wu, Chris Bailey, Parham Rasoulinejad, Shuo Li
Automated quantitative estimation of spinal curvature is an important task for the ongoing evaluation and treatment planning of Adolescent Idiopathic Scoliosis (AIS). It solves the widely accepted disadvantage of manual Cobb angle measurement (time-consuming and unreliable) which is currently the gold standard for AIS assessment. Attempts have been made to improve the reliability of automated Cobb angle estimation. However, it is very challenging to achieve accurate and robust estimation of Cobb angles due to the need for correctly identifying all the required vertebrae in both Anterior-posterior (AP) and Lateral (LAT) view x-rays...
May 18, 2018: Medical Image Analysis
Yongchao Xu, Baptiste Morel, Sonia Dahdouh, Élodie Puybareau, Alessio Virzì, Héléne Urien, Thierry Géraud, Catherine Adamsbaum, Isabelle Bloch
Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value. The aim of this paper is to propose a new semi-automated framework to detect DEHSI on neonatal brain MR images having a particular pattern due to the physiological lack of complete myelination of the white matter...
May 17, 2018: Medical Image Analysis
Jwala Dhamala, Hermenegild J Arevalo, John Sapp, B Milan Horácek, Katherine C Wu, Natalia A Trayanova, Linwei Wang
Model personalization requires the estimation of patient-specific tissue properties in the form of model parameters from indirect and sparse measurement data. Moreover, a low-dimensional representation of the parameter space is needed, which often has a limited ability to reveal the underlying tissue heterogeneity. As a result, significant uncertainty can be associated with the estimated values of the model parameters which, if left unquantified, will lead to unknown variability in model outputs that will hinder their reliable clinical adoption...
May 17, 2018: Medical Image Analysis
Nicola Amoroso, Marianna La Rocca, Alfonso Monaco, Roberto Bellotti, Sabina Tangaro
Parkinson's disease (PD) is the most common neurological disorder, after Alzheimer's disease, and is characterized by a long prodromal stage lasting up to 20 years. As age is a prominent factor risk for the disease, next years will see a continuous increment of PD patients, making urgent the development of efficient strategies for early diagnosis and treatments. We propose here a novel approach based on complex networks for accurate early diagnoses using magnetic resonance imaging (MRI) data; our approach also allows us to investigate which are the brain regions mostly affected by the disease...
May 17, 2018: Medical Image Analysis
Hassan Al Hajj, Mathieu Lamard, Pierre-Henri Conze, Béatrice Cochener, Gwenolé Quellec
This paper investigates the automatic monitoring of tool usage during a surgery, with potential applications in report generation, surgical training and real-time decision support. Two surgeries are considered: cataract surgery, the most common surgical procedure, and cholecystectomy, one of the most common digestive surgeries. Tool usage is monitored in videos recorded either through a microscope (cataract surgery) or an endoscope (cholecystectomy). Following state-of-the-art video analysis solutions, each frame of the video is analyzed by convolutional neural networks (CNNs) whose outputs are fed to recurrent neural networks (RNNs) in order to take temporal relationships between events into account...
May 9, 2018: Medical Image Analysis
Guillermo Ruiz, Eduard Ramon, Jaime García, Federico M Sukno, Miguel A González Ballester
The use of 3D imaging has increased as a practical and useful tool for plastic and aesthetic surgery planning. Specifically, the possibility of representing the patient breast anatomy in a 3D shape and simulate aesthetic or plastic procedures is a great tool for communication between surgeon and patient during surgery planning. For the purpose of obtaining the specific 3D model of the breast of a patient, model-based reconstruction methods can be used. In particular, 3D morphable models (3DMM) are a robust and widely used method to perform 3D reconstruction...
May 2, 2018: Medical Image Analysis
Cesare Corrado, Steven Williams, Rashed Karim, Gernot Plank, Mark O'Neill, Steven Niederer
Biophysical models of the atrium provide a physically constrained framework for describing the current state of an atrium and allow predictions of how that atrium will respond to therapy. We propose a work flow to simulate patient specific electrophysiological heterogeneity from clinical data and validate the resulting biophysical models. In 7 patients, we recorded the atrial anatomy with an electroanatomical mapping system (St Jude Velocity); we then applied an S1-S2 electrical stimulation protocol from the coronary sinus (CS) and the high right atrium (HRA) whilst recording the activation patterns using a PentaRay catheter with 10 bipolar electrodes at 12 ± 2 sites across the atrium...
April 27, 2018: Medical Image Analysis
Yu Zhao, Fangfei Ge, Tianming Liu
fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities...
April 26, 2018: Medical Image Analysis
Ruobing Huang, Weidi Xie, J Alison Noble
Three-dimensional (3D) fetal neurosonography is used clinically to detect cerebral abnormalities and to assess growth in the developing brain. However, manual identification of key brain structures in 3D ultrasound images requires expertise to perform and even then is tedious. Inspired by how sonographers view and interact with volumes during real-time clinical scanning, we propose an efficient automatic method to simultaneously localize multiple brain structures in 3D fetal neurosonography. The proposed View-based Projection Networks (VP-Nets), uses three view-based Convolutional Neural Networks (CNNs), to simplify 3D localizations by directly predicting 2D projections of the key structures onto three anatomical views...
April 23, 2018: Medical Image Analysis
Assaf Arbelle, Jose Reyes, Jia-Yun Chen, Galit Lahav, Tammy Riklin Raviv
We present a novel computational framework for the analysis of high-throughput microscopy videos of living cells. The proposed framework is generally useful and can be applied to different datasets acquired in a variety of laboratory settings. This is accomplished by tying together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. In contrast to most existing approaches, which aim to be general, no assumption of cell shape is made...
April 22, 2018: Medical Image Analysis
Tarun Gangwar, Jeff Calder, Takashi Takahashi, Joan E Bechtold, Dominik Schillinger
We present a two-stage variational approach for segmenting 3D bone CT data that performs robustly with respect to thin cartilage interfaces. In the first stage, we minimize a flux-augmented Chan-Vese model that accurately segments well-separated regions. In the second stage, we apply a new phase-field fracture inspired model that reliably eliminates spurious bridges across thin cartilage interfaces, resulting in an accurate segmentation topology, from which each bone object can be identified. Its mathematical formulation is based on the phase-field approach to variational fracture, which naturally blends with the variational approach to segmentation...
April 17, 2018: Medical Image Analysis
Thomas E Fastl, Catalina Tobon-Gomez, Andrew Crozier, John Whitaker, Ronak Rajani, Karen P McCarthy, Damian Sanchez-Quintana, Siew Y Ho, Mark D O'Neill, Gernot Plank, Martin J Bishop, Steven A Niederer
Atrial fibrillation (AF) is a supraventricular tachyarrhythmia characterized by complete absence of coordinated atrial contraction and is associated with an increased morbidity and mortality. Personalized computational modeling provides a novel framework for integrating and interpreting the role of atrial electrophysiology (EP) including the underlying anatomy and microstructure in the development and sustenance of AF. Coronary computed tomography angiography data were segmented using a statistics-based approach and the smoothed voxel representations were discretized into high-resolution tetrahedral finite element (FE) meshes...
April 5, 2018: Medical Image Analysis
Biao Jie, Mingxia Liu, Dinggang Shen
Functional connectivity networks (FCNs) using resting-state functional magnetic resonance imaging (rs-fMRI) have been applied to the analysis and diagnosis of brain disease, such as Alzheimer's disease (AD) and its prodrome, i.e., mild cognitive impairment (MCI). Different from conventional studies focusing on static descriptions on functional connectivity (FC) between brain regions in rs-fMRI, recent studies have resorted to dynamic connectivity networks (DCNs) to characterize the dynamic changes of FC, since dynamic changes of FC may indicate changes in macroscopic neural activity patterns in cognitive and behavioral aspects...
April 4, 2018: Medical Image Analysis
Jian Wu, Thomas R Mazur, Su Ruan, Chunfeng Lian, Nalini Daniel, Hilary Lashmett, Laura Ochoa, Imran Zoberi, Mark A Anastasio, H Michael Gach, Sasa Mutic, Maria Thomas, Hua Li
Heart motion tracking for radiation therapy treatment planning can result in effective motion management strategies to minimize radiation-induced cardiotoxicity. However, automatic heart motion tracking is challenging due to factors that include the complex spatial relationship between the heart and its neighboring structures, dynamic changes in heart shape, and limited image contrast, resolution, and volume coverage. In this study, we developed and evaluated a deep generative shape model-driven level set method to address these challenges...
July 2018: Medical Image Analysis
Azam Hamidinekoo, Erika Denton, Andrik Rampun, Kate Honnor, Reyer Zwiggelaar
Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based CAD systems developed for mammography and breast histopathology images is presented. In this study, the relationship between mammography and histopathology phenotypes is described, which takes biological aspects into account...
July 2018: Medical Image Analysis
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