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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 ..
Wenfeng Xia, Sacha Noimark, Sebastien Ourselin, Simeon J West, Malcolm C Finlay, Anna L David, Adrien E Desjardins
Ultrasound imaging is widely used for guiding minimally invasive procedures, including fetal surgery. Visualisation of medical devices such as medical needles is critically important and it remains challenging in many clinical contexts. During in-plane insertions, a needle can have poor visibility at steep insertion angles and at large insertion depths. During out-of-plane insertions, the needle tip can have a similar ultrasonic appearance to the needle shaft when it intersects with the ultrasound imaging plane...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Chuyang Ye, Jerry L Prince
Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN)...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Jeffrey Glaister, Aaron Carass, Dzung L Pham, John A Butman, Jerry L Prince
The falx cerebri is a meningeal projection of dura in the brain, separating the cerebral hemispheres. It has stiffer mechanical properties than surrounding tissue and must be accurately segmented for building computational models of traumatic brain injury. In this work, we propose a method to segment the falx using T1-weighted magnetic resonance images (MRI) and susceptibility-weighted MRI (SWI). Multi-atlas whole brain segmentation is performed using the T1-weighted MRI and the gray matter cerebrum labels are extended into the longitudinal fissure using fast marching to find an initial estimate of the falx...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Amod Jog, Aaron Carass, Jerry L Prince
It is faster and therefore cheaper to acquire magnetic resonance images (MRI) with higher in-plane resolution than through-plane resolution. The low resolution of such acquisitions can be increased using post-processing techniques referred to as super-resolution (SR) algorithms. SR is known to be an ill-posed problem. Most state-of-the-art SR algorithms rely on the presence of external/training data to learn a transform that converts low resolution input to a higher resolution output. In this paper an SR approach is presented that is not dependent on any external training data and is only reliant on the acquired image...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Seth D Billings, Ayushi Sinha, Austin Reiter, Simon Leonard, Masaru Ishii, Gregory D Hager, Russell H Taylor
Functional endoscopic sinus surgery (FESS) is a surgical procedure used to treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming the preferred choice of treatment due to its minimally invasive nature. However, due to the limited field of view of the endoscope, surgeons rely on navigation systems to guide them within the nasal cavity. State of the art navigation systems report registration accuracy of over 1mm, which is large compared to the size of the nasal airways. We present an anatomically constrained video-CT registration algorithm that incorporates multiple video features...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
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