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Medical Image Analysis

Xiao-Yun Zhou, Guang-Zhong Yang, Su-Lin Lee
Real-time 3D navigation during minimally invasive procedures is an essential yet challenging task, especially when considerable tissue motion is involved. To balance image acquisition speed and resolution, only 2D images or low-resolution 3D volumes can be used clinically. In this paper, a real-time and registration-free framework for dynamic shape instantiation, generalizable to multiple anatomical applications, is proposed to instantiate high-resolution 3D shapes of an organ from a single 2D image intra-operatively...
November 30, 2017: Medical Image Analysis
Majd Zreik, Nikolas Lessmann, Robbert W van Hamersvelt, Jelmer M Wolterink, Michiel Voskuil, Max A Viergever, Tim Leiner, Ivana Išgum
In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA)...
November 26, 2017: Medical Image Analysis
Chuyang Ye, Jerry L Prince
Diffusion magnetic resonance imaging (dMRI) has enabled in vivo investigation of white matter tracts. Fiber orientation (FO) estimation is a key step in tract reconstruction and has been a popular research topic in dMRI analysis. In particular, the sparsity assumption has been used in conjunction with a dictionary-based framework to achieve reliable FO estimation with a reduced number of gradient directions. Because image noise can have a deleterious effect on the accuracy of FO estimation, previous works have incorporated spatial consistency of FOs in the dictionary-based framework to improve the estimation...
November 23, 2017: Medical Image Analysis
John S H Baxter, Eli Gibson, Roy Eagleson, Terry M Peters
As the interaction between clinicians and computational processes increases in complexity, more nuanced mechanisms are required to describe how their communication is mediated. Medical image segmentation in particular affords a large number of distinct loci for interaction which can act on a deep, knowledge-driven level which complicates the naive interpretation of the computer as a symbol processing machine. Using the perspective of the computer as dialogue partner, we can motivate the semiotic understanding of medical image segmentation...
November 21, 2017: Medical Image Analysis
Jiawen Yao, Zheng Xu, Xiaolei Huang, Junzhou Huang
In this paper, we propose an efficient algorithm for dynamic magnetic resonance (MR) image reconstruction. With the total variation (TV) and the nuclear norm (NN) regularization, the TVNNR model can utilize both spatial and temporal redundancy in dynamic MR images. Such prior knowledge can help model dynamic MRI data significantly better than a low-rank or a sparse model alone. However, it is very challenging to efficiently minimize the energy function due to the non-smoothness and non-separability of both TV and NN terms...
November 17, 2017: Medical Image Analysis
R A Pearson, G M Treece
The extent of the endocortical region and cortical bone mineral density (cBMD) throughout the proximal femur are of interest as both have been linked to fracture risk and osteoporosis treatment response. Non-invasive in-vivo clinical CT-based techniques capable of measuring the cortical bone attributes of thickness, density and mass over a bone surface have already been proposed. Several studies have robustly shown these methods to be capable of producing cortical thickness measurements to a sub-millimetre accuracy...
November 16, 2017: Medical Image Analysis
Michal Drozdzal, Gabriel Chartrand, Eugene Vorontsov, Mahsa Shakeri, Lisa Di Jorio, An Tang, Adriana Romero, Yoshua Bengio, Chris Pal, Samuel Kadoury
In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importance of a trainable pre-processing when using FC-ResNets and we show that a low-capacity FCN model can serve as a pre-processor to normalize medical input data...
November 14, 2017: Medical Image Analysis
Benjamin Thyreau, Kazunori Sato, Hiroshi Fukuda, Yasuyuki Taki
The hippocampus is a particularly interesting target for neuroscience research studies due to its essential role within the human brain. In large human cohort studies, bilateral hippocampal structures are frequently identified and measured to gain insight into human behaviour or genomic variability in neuropsychiatric disorders of interest. Automatic segmentation is performed using various algorithms, with FreeSurfer being a popular option. In this manuscript, we present a method to segment the bilateral hippocampus using a deep-learned appearance model...
November 10, 2017: Medical Image Analysis
Zhengwang Wu, Yanrong Guo, Sang Hyun Park, Yaozong Gao, Pei Dong, Seong-Whan Lee, Dinggang Shen
We propose a robust and efficient learning-based deformable model for segmenting regions of interest (ROIs) from structural MR brain images. Different from the conventional deformable-model-based methods that deform a shape model locally around the initialization location, we learn an image-based regressor to guide the deformable model to fit for the target ROI. Specifically, given any voxel in a new image, the image-based regressor can predict the displacement vector from this voxel towards the boundary of target ROI, which can be used to guide the deformable segmentation...
November 10, 2017: Medical Image Analysis
Cesare Corrado, Nejib Zemzemi
Computational models of heart electrophysiology achieved a considerable interest in the medical community as they represent a novel framework for the study of the mechanisms underpinning heart pathologies. The high demand of computational resources and the long computational time required to evaluate the model solution hamper the use of detailed computational models in clinical applications. In this paper, we present a multi-front eikonal algorithm that adapts the conduction velocity (CV) to the activation frequency of the tissue substrate...
November 3, 2017: Medical Image Analysis
Matthew Sinclair, Devis Peressutti, Esther Puyol-Antón, Wenjia Bai, Simone Rivolo, Jessica Webb, Simon Claridge, Thomas Jackson, David Nordsletten, Myrianthi Hadjicharalambous, Eric Kerfoot, Christopher A Rinaldi, Daniel Rueckert, Andrew P King
Abnormal cardiac motion can indicate different forms of disease, which can manifest at different spatial scales in the myocardium. Many studies have sought to characterise particular motion abnormalities associated with specific diseases, and to utilise motion information to improve diagnoses. However, the importance of spatial scale in the analysis of cardiac deformation has not been extensively investigated. We build on recent work on the analysis of myocardial strains at different spatial scales using a cardiac motion atlas to find the optimal scales for estimating different cardiac biomarkers...
October 31, 2017: Medical Image Analysis
Lazaros Vlachopoulos, Gábor Székely, Christian Gerber, Philipp Fürnstahl
The optimal surgical treatment of complex fractures of the proximal humerus is controversial. It is proven that best results are obtained if an anatomical reduction of the fragments is achieved and, therefore, computer-assisted methods have been proposed for the reconstruction of the fractures. However, complex fractures of the proximal humerus are commonly accompanied with a relevant displacement of the fragments and, therefore, algorithms relying on the initial position of the fragments might fail. The state-of-the-art algorithm for complex fractures of the proximal humerus requires the acquisition of a CT scan of the (healthy) contralateral anatomy as a reconstruction template to address the displacement of the fragments...
October 27, 2017: Medical Image Analysis
Xènia Albà, Karim Lekadir, Marco Pereañez, Pau Medrano-Gracia, Alistair A Young, Alejandro F Frangi
Continuous advances in imaging technologies enable ever more comprehensive phenotyping of human anatomy and physiology. Concomitant reduction of imaging costs has resulted in widespread use of imaging in large clinical trials and population imaging studies. Magnetic Resonance Imaging (MRI), in particular, offers one-stop-shop multidimensional biomarkers of cardiovascular physiology and pathology. A wide range of analysis methods offer sophisticated cardiac image assessment and quantification for clinical and research studies...
October 16, 2017: Medical Image Analysis
Andrew Lang, Aaron Carass, Bruno M Jedynak, Sharon D Solomon, Peter A Calabresi, Jerry L Prince
Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer...
October 12, 2017: Medical Image Analysis
Jian Cheng, Peter J Basser
In Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI), a tensor field or a spherical function field (e.g., an orientation distribution function field), can be estimated from measured diffusion weighted images. In this paper, inspired by the microscopic theoretical treatment of phases in liquid crystals, we introduce a novel mathematical framework, called Director Field Analysis (DFA), to study local geometric structural information of white matter based on the reconstructed tensor field or spherical function field: (1) We propose a set of mathematical tools to process general director data, which consists of dyadic tensors that have orientations but no direction...
October 11, 2017: Medical Image Analysis
Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yazhuo Zhang, Yong Fan
Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices...
October 5, 2017: Medical Image Analysis
Zhongyu Li, Xiaofan Zhang, Henning Müller, Shaoting Zhang
Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval...
October 2, 2017: Medical Image Analysis
Wufeng Xue, Gary Brahm, Sachin Pandey, Stephanie Leung, Shuo Li
Cardiac left ventricle (LV) quantification is among the most clinically important tasks for identification and diagnosis of cardiac disease. However, it is still a task of great challenge due to the high variability of cardiac structure across subjects and the complexity of temporal dynamics of cardiac sequences. Full quantification, i.e., to simultaneously quantify all LV indices including two areas (cavity and myocardium), six regional wall thicknesses (RWT), three LV dimensions, and one phase (Diastole or Systole), is even more challenging since the ambiguous correlations existing among these indices may impinge upon the convergence and generalization of the learning procedure...
September 28, 2017: Medical Image Analysis
Martin Urschler, Thomas Ebner, Darko Štern
In approaches for automatic localization of multiple anatomical landmarks, disambiguation of locally similar structures as obtained by locally accurate candidate generation is often performed by solely including high level knowledge about geometric landmark configuration. In our novel localization approach, we propose to combine both image appearance information and geometric landmark configuration into a unified random forest framework integrated into an optimization procedure that iteratively refines joint landmark predictions by using the coordinate descent algorithm...
September 21, 2017: Medical Image Analysis
Zhengwang Wu, Yaozong Gao, Feng Shi, Guangkai Ma, Valerie Jewells, Dinggang Shen
Hippocampal subfields play important roles in many brain activities. However, due to the small structural size, low signal contrast, and insufficient image resolution of 3T MR, automatic hippocampal subfields segmentation is less explored. In this paper, we propose an automatic learning-based hippocampal subfields segmentation method using 3T multi-modality MR images, including structural MRI (T1, T2) and resting state fMRI (rs-fMRI). The appearance features and relationship features are both extracted to capture the appearance patterns in structural MR images and also the connectivity patterns in rs-fMRI, respectively...
September 21, 2017: Medical Image Analysis
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