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Medical Image Computing and Computer-assisted Intervention: MICCAI ...

Antonio R Porras, Beatriz Paniagua, Andinet Enquobahrie, Scott Ensel, Hina Shah, Robert Keating, Gary F Rogers, Marius George Linguraru
The outcome of cranial vault reconstruction for the surgical treatment of craniosynostosis heavily depends on the surgeon's expertise because of the lack of an objective target shape. We introduce a surface-based diffeomorphic registration framework to create the optimal post-surgical cranial shape during craniosynostosis treatment. Our framework estimates and labels where each bone piece needs to be cut using a reference template. Then, it calculates how much each bone piece needs to be translated and in which direction, using the closest normal shape from a multi-atlas as a reference...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Xiaofeng Zhu, Kim-Han Thung, Ehsan Adeli, Yu Zhang, Dinggang Shen
It is challenging to use incomplete multimodality data for Alzheimer's Disease (AD) diagnosis. The current methods to address this challenge, such as low-rank matrix completion (i.e., imputing the missing values and unknown labels simultaneously) and multi-task learning (i.e., defining one regression task for each combination of modalities and then learning them jointly), are unable to model the complex data-to-label relationship in AD diagnosis and also ignore the heterogeneity among the modalities. In light of this, we propose a new Maximum Mean Discrepancy (MMD) based Multiple Kernel Learning (MKL) method for AD diagnosis using incomplete multimodality data...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yi Hong, Polina Golland, Miaomiao Zhang
Geodesic regression on images enables studies of brain development and degeneration, disease progression, and tumor growth. The high-dimensional nature of image data presents significant computational challenges for the current regression approaches and prohibits large scale studies. In this paper, we present a fast geodesic regression method that dramatically decreases the computational cost of the inference procedure while maintaining prediction accuracy. We employ an efficient low dimensional representation of diffeomorphic transformations derived from the image data and characterize the regressed trajectory in the space of diffeomorphisms by its initial conditions, i...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Jun Zhang, Mingxia Liu, Li Wang, Si Chen, Peng Yuan, Jianfu Li, Steve Guo-Fang Shen, Zhen Tang, Ken-Chung Chen, James J Xia, Dinggang Shen
Generating accurate 3D models from cone-beam computed tomography (CBCT) images is an important step in developing treatment plans for patients with craniomaxillofacial (CMF) deformities. This process often involves bone segmentation and landmark digitization. Since anatomical landmarks generally lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly correlated. However, most existing methods simply treat them as two standalone tasks, without considering their inherent association...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Jie Yang, Elsa D Angelini, Pallavi P Balte, Eric A Hoffman, John H M Austin, Benjamin M Smith, Jingkuan Song, R Graham Barr, Andrew F Laine
Unsupervised discovery of pulmonary emphysema subtypes offers the potential for new definitions of emphysema on lung computed tomography (CT) that go beyond the standard subtypes identified on autopsy. Emphysema subtypes can be defined on CT as a variety of textures with certain spatial prevalence. However, most existing approaches for learning emphysema subtypes on CT are limited to texture features, which are sub-optimal due to the lack of spatial information. In this work, we exploit a standardized spatial mapping of the lung and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs)...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Xinyang Feng, Jie Yang, Andrew F Laine, Elsa D Angelini
Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for automated nodule segmentation on CT scans require voxel-based annotations for training, which are labor- and time-consuming to obtain. In this work, we propose a weakly-supervised method that generates accurate voxel-level nodule segmentation trained with image-level labels only. By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and identifies the true nodule location with a novel candidate-screening framework...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Felix J S Bragman, Jamie R McClelland, Joseph Jacob, John R Hurst, David J Hawkes
Analysis of CT scans for studying Chronic Obstructive Pulmonary Disease (COPD) is generally limited to mean scores of disease extent. However, the evolution of local pulmonary damage may vary between patients with discordant effects on lung physiology. This limits the explanatory power of mean values in clinical studies. We present local disease and deformation distributions to address this limitation. The disease distribution aims to quantify two aspects of parenchymal damage: locally diffuse/dense disease and global homogeneity/heterogeneity...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Alison M Pouch, Ahmed H Aly, Eric K Lai, Natalie Yushkevich, Rutger H Stoffers, Joseph H Gorman, Albert T Cheung, Joseph H Gorman, Robert C Gorman, Paul A Yushkevich
Transesophageal echocardiography is the primary imaging modality for preoperative assessment of mitral valves with ischemic mitral regurgitation (IMR). While there are well known echocardiographic insights into the 3D morphology of mitral valves with IMR, such as annular dilation and leaflet tethering, less is understood about how quantification of valve dynamics can inform surgical treatment of IMR or predict short-term recurrence of the disease. As a step towards filling this knowledge gap, we present a novel framework for 4D segmentation and geometric modeling of the mitral valve in real-time 3D echocardiography (rt-3DE)...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Xiaohuan Cao, Jianhua Yang, Jun Zhang, Dong Nie, Min-Jeong Kim, Qian Wang, Dinggang Shen
Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Prasanna Parvathaneni, Baxter P Rogers, Yuankai Huo, Kurt G Schilling, Allison E Hainline, Adam W Anderson, Neil D Woodward, Bennett A Landman
Tract-based spatial statistics (TBSS) has proven to be a popular technique for performing voxel-wise statistical analysis that aims to improve sensitivity and interpretability of analysis of multi-subject diffusion imaging studies in white matter. With the advent of advanced diffusion MRI models - e.g., the neurite orientation dispersion density imaging (NODDI), it is of interest to analyze microstructural changes within gray matter (GM). A recent study has proposed using NODDI in gray matter based spatial statistics (N-GBSS) to perform voxel-wise statistical analysis on GM microstructure...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yujia Zhou, Pew-Thian Yap, Han Zhang, Lichi Zhang, Qianjin Feng, Dinggang Shen
Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) largely rely on the accurate inter-subject registration of functional areas. This is typically achieved through registration of the corresponding T1-weighted MR images with more structural details. However, accumulating evidence has suggested that such strategy cannot well-align functional regions which are not necessarily confined by the anatomical boundaries defined by the T1-weighted MR images. To mitigate this problem, various registration algorithms based directly on rs-fMRI data have been developed, most of which have utilized functional connectivity (FC) as features for registration...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Gang Li, Li Wang, Weili Lin, Dinggang Shen
The human cerebral cortex develops dynamically during the early postnatal stage, reflecting the underlying rapid changes of cortical microstructures and their connections, which jointly determine the functional principles of cortical regions. Hence, the dynamic cortical developmental patterns are ideal for defining the distinct cortical regions in microstructure and function for neurodevelopmental studies. Moreover, given the remarkable inter-subject variability in terms of cortical structure/function and their developmental patterns, the individualized cortical parcellation based on each infant's own developmental patterns is critical for precisely localizing personalized distinct cortical regions and also understanding inter-subject variability...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Dingna Duan, Shunren Xia, Yu Meng, Li Wang, Weili Lin, John H Gilmore, Dinggang Shen, Gang Li
The human cortical folding is intriguingly complex in its variability and regularity across individuals. Exploring the principal patterns of cortical folding is of great importance for neuroimaging research. The term-born neonates with minimum exposure to the complicated environments are the ideal candidates to mine the postnatal origins of principal cortical folding patterns. In this work, we propose a novel framework to study the gyral patterns of neonatal cortical folding. Specifically, first, we leverage multi-view curvature-derived features to comprehensively characterize the complex and multi-scale nature of cortical folding...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Zhengwang Wu, Gang Li, Yu Meng, Li Wang, Weili Lin, Dinggang Shen
The 4D infant cortical surface atlas with densely sampled time points is highly needed for neuroimaging analysis of early brain development. In this paper, we build the 4D infant cortical surface atlas firstly covering 6 postnatal years with 11 time points (i.e., 1, 3, 6, 9, 12, 18, 24, 36, 48, 60, and 72 months), based on 339 longitudinal MRI scans from 50 healthy infants. To build the 4D cortical surface atlas, first, we adopt a two-stage groupwise surface registration strategy to ensure both longitudinal consistency and unbiasedness...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Pramod Kumar Pisharady, Stamatios N Sotiropoulos, Guillermo Sapiro, Christophe Lenglet
We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Anand A Joshi, Minqi Chong, Richard M Leahy
We describe a method that allows direct comparison of resting fMRI (rfMRI) time series across subjects. For this purpose, we exploit the geometry of the rfMRI signal space to conjecture the existence of an orthogonal transformation that synchronizes fMRI time series across sessions and subjects. The method is based on the observation that rfMRI data exhibit similar connectivity patterns across subjects, as reflected in the pairwise correlations between different brain regions. The orthogonal transformation that performs the synchronization is unique, invertible, efficient to compute, and preserves the connectivity structure of the original data for all subjects...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Nicolas Honnorat, Drew Parker, Birkan Tunç, Christos Davatzikos, Ragini Verma
Brain parcellation provides a means to approach the brain in smaller regions. It also affords an appropriate dimensionality reduction in the creation of connectomes. Most approaches to creating connectomes start with registering individual scans to a template, which is then parcellated. Data processing usually ends with the projection of individual scans onto the parcellation for extracting individual biomarkers, such as connectivity signatures. During this process, registration errors can significantly alter the quality of biomarkers...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Junyan Wang, Yonggang Shi
Topographic regularity is a fundamental property in brain connectivity. In this work, we present a novel method for studying topographic regularity of functional connectivity based on resting-state fMRI (rfMRI), which is widely available and easy to acquire in large-scale studies. The main idea in our method is the incorporation of topographically regular structural connectivity for independent component analysis (ICA). This is enabled by the recent development of novel tractography and tract filtering algorithms that can generate highly organized fiber bundles connecting different brain regions...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Wei Sun, Lilyana Amezcua, Yonggang Shi
To achieve improved understanding of white matter (WM) lesions and their effect on brain functions, it is important to obtain a comprehensive map of their connectivity. However, changes of the cellular environment in WM lesions attenuate diffusion MRI (dMRI) signals and make the robust estimation of fiber orientation distributions (FODs) difficult. In this work, we integrate techniques from image inpainting and compartment modeling to develop a novel method for enhancing FOD estimation in WM lesions from multi-shell dMRI, which is becoming increasingly popular with the success of the Human Connectome Project (HCP)...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Jin Kyu Gahm, Yonggang Shi
In brain shape analysis, the striatum is typically divided into three parts: the caudate, putamen, and accumbens nuclei for its analysis. Recent connectivity and animal studies, however, indicate striatum-cortical inter-connections do not always follow such subdivisions. For the holistic mapping of striatum surfaces, conventional spherical registration techniques are not suitable due to the large metric distortions in spherical parameterization of striatal surfaces. To overcome this difficulty, we develop a novel striatal surface mapping method using the recently proposed Riemannian metric optimization techniques in the Laplace-Beltrami (LB) embedding space...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
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